rm(list = ls())
pacman::p_load(tidyverse,
hrbrthemes, 
patchwork,
here,
stringr,
knitr,
formatR,
forcats,
ggplot2,
bibliometrix,
igraph,
stringi,
stringdist,
circlize,
ggalluvial,
ggraph)
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)

# Load CSV datasets
sd <- read_csv(here("data", "ocp_srm_study_details.csv"))
ocp <- read_csv(here("data", "ocp_srm_ocp_details.csv"))
sub <- read_csv(here("data", "ocp_srm_subject_details.csv"))
im <- read_csv(here("data", "ocp_srm_impact_details.csv"))
sp <- read_csv(here("data", "ocp_srm_species_details.csv"))

# Load BibTeX dataset
bib_sco <- convert2df(here("data", "bib_sco.bib"), dbsource = "scopus", format = "bibtex")

# Join the study and subject datasets
sd_sub <- left_join(sd, sub, by = "study_id")

# Count the number of publications per year and calculate cumulative sum
sd1 <- sd %>%
  count(publication_year) %>%
  mutate(n_cumulative = cumsum(n))


# Create a theme for the count plots 
theme_count <- function() {
  theme_minimal() +
  theme(
    panel.grid.major.y = element_line(color = "gray", linetype = "dashed"),
    panel.grid.minor.y = element_blank(),
    axis.line.x = element_line(size = 1.2),
    axis.line.y = element_line(size = 1.2),
    axis.title.x = element_text(size = 20, face = "bold"),
    axis.title.y = element_text(size = 20, face = "bold"),
    axis.text.x = element_text(angle = 45, hjust = 1, size = 15, color = "#666666"),
    axis.text.y = element_text(size = 20, color = "#666666"),
    legend.position = "none"
  )
}


# Create the annual count plot
fig1a <- sd_sub %>%
  mutate(subjects = strsplit(subject, ",\\s+")) %>%
  unnest(subjects) %>%
  count(publication_year, subjects) %>%
  group_by(subjects = reorder(subjects, n)) %>%
  ggplot(aes(x = publication_year, y = n, fill = subjects)) +
    geom_bar(stat = "identity", position = "stack", alpha = 0.7) +
    geom_text(aes(label = n), position = position_stack(vjust = 0.6), 
              fontface = "bold", color = "white", size = 6, hjust = 0.4) +
    scale_x_continuous(breaks = seq(min(sd1$publication_year), max(sd1$publication_year), by = 1), 
                       expand = expansion(0,0)) +  
    scale_y_continuous("Annual Article Count", limits = c(0,15)) +
    labs(y = "Article Count", tag = "A", fill = "Subjects") +
    theme_count() +
    theme(axis.title.x = element_blank()) +
    scale_fill_brewer(palette = "Dark2") +
    guides(fill = guide_legend(override.aes = list(size=10)))


# Create the cumulative count plot
fig1b <- sd_sub %>%
  mutate(subjects = strsplit(subject, ",\\s+")) %>%
  unnest(subjects) %>%
  count(publication_year, subjects) %>%
  group_by(subjects = reorder(subjects, n)) %>%
  mutate(n_cumulative = cumsum(n)) %>%
  ggplot(aes(x = publication_year, y = n_cumulative, fill = subjects)) +
    geom_area(size = 1.2, alpha = 0.7) +
    scale_y_continuous("Cumulative Article Count", limits = c(0,115)) +
    scale_x_continuous(breaks = seq(min(sd_sub$publication_year), max(sd_sub$publication_year), by = 1), 
                       expand = expansion(0,0)) +  
    labs(x = "Year", y = "Cumulative Article Count", tag = "B", fill = "Subjects") +
    theme_count() +
    scale_fill_brewer(palette = "Dark2") +
    guides(fill = guide_legend(override.aes = list(size=10)))

# Combine the two plots
fig1 <- fig1a / fig1b
fig1

# ggsave(here("figures", "fig1.pdf"), width = 16, height = 12, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "fig1.jpg"), width = 16, height = 12, units = "cm", scale = 2, dpi = 800)


# Total number of included primary literature
 total_sum_primary <- sd %>%
  summarise(total_sum = sum(total_included_studies, na.rm = TRUE)) %>%
  pull(total_sum)

 total_sum_primary # 3911

## [1] 3911

organochlorTHEME <- function() {
  theme_minimal() +
  theme(
    title = element_text(size = 30),
    axis.text.y = element_text(size = 30),
    axis.text.x = element_text(size = 20),
    axis.line.x = element_line(color = "gray", size = 3),
    axis.line.y = element_blank(),
    axis.ticks.x = element_blank(),
    axis.ticks.y = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.y = element_blank(),
    legend.position = "none")    
}

# Create the annual Count Plot
figs4a <- sd1 %>%
  ggplot(aes(x = publication_year, y = n)) +
    geom_col(fill = "#1b9e77", alpha = 0.7) +
    geom_text(aes(label = n), position = position_stack(vjust = 0.6), 
              fontface = "bold", color = "white", size = 6, hjust = 0.4) +
    scale_x_continuous(breaks = seq(min(sd1$publication_year), max(sd1$publication_year), by = 1), 
                       expand = expansion(0,1)) +
    scale_y_continuous("Annual Article Count", limits = c(0,15)) +
    labs(x = "Year", tag = "A") +
    theme_count() +
    theme(axis.title.x = element_blank())


# Create the cumulative Count Plot
figs4b <- sd1 %>%
  ggplot(aes(x = publication_year, y = n_cumulative)) +
    geom_line(color = "#7570b3", size = 1, linetype = "solid") +
    geom_point(shape = 21, size = 6, fill = "#7570b3", stroke = 0) +
    geom_text(aes(label = n_cumulative), hjust = 0.2, vjust = -1, size = 6, color = "black") +
    scale_x_continuous(breaks = seq(min(sd1$publication_year), max(sd1$publication_year), by = 1), 
                       expand = expansion(0,1)) +
    scale_y_continuous("Cumulative Article Count", limits = c(0,120)) +
    labs(x = "Year", tag = "B") +
    theme_count()

# Combine the two plots
figs4 <- figs4a / figs4b
figs4

# ggsave(here("figures", "figs4.pdf"), width = 16, height = 12, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs4.jpg"), width = 16, height = 12, units = "cm", scale = 2, dpi = 800)

# Start the data manipulation
percent_ceesat_score <- sd %>%
  filter(!is.na(author_year)) %>%
  select(studies = author_year, starts_with("CEE")) %>%
  na.omit() %>%
  pivot_longer(cols = -studies, names_to = "question", values_to = "score") %>%
  group_by(question, score) %>%
  summarise(n = n(), .groups = 'drop') %>%
  mutate(percent = (n/sum(n))*100, 
         across(c(question, score), as.factor),
         question = fct_recode(question, 
           `1.1 Are the elements of the review question clear?` = "CEESAT2_1.1",
           `2.1 Is there an a-priori method protocol document?` = "CEESAT2_2.1",
           `3.1. Is the approach to searching clearly defined systematic and\ntransparent?` = "CEESAT2_3.1",
           `3.2. Is the search comprehensive?` = "CEESAT2_3.2",
           `4.1. Are eligibility criteria clearly defined?` = "CEESAT2_4.1",
           `4.2. Are eligibility criteria consistently applied to all potentially relevant\narticles and studies found during the search?` = "CEESAT2_4.2",
           `4.3. Are eligibility decisions transparently reported?` = "CEESAT2_4.3",
           `5.1. Does the review critically appraise each study?` = "CEESAT2_5.1",
           `5.2. During critical appraisal was an effort made to minimise\nsubjectivity?` = "CEESAT2_5.2",
           `6.1. Is the method of data extraction fully documented?` = "CEESAT2_6.1",
           `6.2. Are the extracted data reported for each study?` = "CEESAT2_6.2",
           `6.3. Were extracted data cross checked by more than one reviewer?` = "CEESAT2_6.3",
           `7.1. Is the choice of synthesis approach appropriate?` = "CEESAT2_7.1",
           `7.2. Is a statistical estimate of pooled effect provided together with\nmeasure of variance and heterogeneity among studies?` = "CEESAT2_7.2",
           `7.3 Is variability in the study findings investigated and discussed?` = "CEESAT2_7.3",
           `8.1 Have the authors considered limitations in the synthesis?` = "CEESAT2_8.1"),
         question = factor(question, levels = rev(levels(question))),
         score = factor(score, levels = levels(score)[c(4,1,3,2)]))

# Create CEESAT plot 
fig2a <- ggplot(data = percent_ceesat_score, aes(x = question, y = percent, fill = score)) +
  geom_col(width = 0.7, position = "fill", color = "black") +
  geom_text(aes(label = n), position = position_fill(vjust = 0.5), size = 7, fontface = "bold") +
  coord_flip() +
  guides(fill = guide_legend(reverse = TRUE)) +
  scale_fill_manual(values = c("#FF0000","#FFD700","#008000", "#DAA520"), name = "Score:") +
  scale_y_continuous(labels = scales::percent) +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        panel.background = element_blank(),
        axis.text.y = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.title.x = element_text(size = 25),
        axis.title.y = element_text(size = 25), 
        legend.position = "top",  
        legend.justification = "left",  
        legend.direction = "horizontal",
        legend.box = "horizontal",  
        legend.box.just = "left",  
        legend.title.align = 0.5,  
        legend.key.size = unit(1, "cm"),  
        legend.title = element_text(size =25),
        legend.text = element_text(size = 25),  
        strip.text = element_text(size = 20),
        strip.background = element_blank(), 
        plot.tag = element_text(size = 25)) + 
  ylab("Percentage") + 
  xlab("CEESAT Question") +  
  labs(tag = "A")

fig2a

total_n <- sum(percent_ceesat_score$n)
percentages <- tibble(
  red_percentage = sum(percent_ceesat_score$n[percent_ceesat_score$score == "Red"]) / total_n * 100,
  amber_percentage = sum(percent_ceesat_score$n[percent_ceesat_score$score == "Amber"]) / total_n * 100,
  green_percentage = sum(percent_ceesat_score$n[percent_ceesat_score$score == "Green"]) / total_n * 100,
  gold_percentage = sum(percent_ceesat_score$n[percent_ceesat_score$score == "Gold"]) / total_n * 100
)

 percentages

## # A tibble: 1 × 4
##   red_percentage amber_percentage green_percentage gold_percentage
##            <dbl>            <dbl>            <dbl>           <dbl>
## 1           32.9             50.5             10.7            5.87

# ggsave(here("figures", "fig2a.pdf"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "fig2a.jpg"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)

# Create new categories for specified questions in numerical order
percent_ceesat_score$combined_category <- ifelse(
  grepl("^1\\.1", percent_ceesat_score$question) | grepl("^2\\.1", percent_ceesat_score$question),
  "Study Question",
  ifelse(grepl("^3\\.1", percent_ceesat_score$question) | grepl("^3\\.2", percent_ceesat_score$question),
    "Literature Searching",
    ifelse(grepl("^4\\.1", percent_ceesat_score$question) | grepl("^4\\.2", percent_ceesat_score$question) | grepl("^4\\.3", percent_ceesat_score$question),
      "Literature Screening",
      ifelse(grepl("^5\\.1", percent_ceesat_score$question) | grepl("^5\\.2", percent_ceesat_score$question) | 
             grepl("^6\\.1", percent_ceesat_score$question) | grepl("^6\\.2", percent_ceesat_score$question) | grepl("^6\\.3", percent_ceesat_score$question),
        "Data Extraction", 
        ifelse(grepl("^7", percent_ceesat_score$question), "Data Analysis", 
          ifelse(grepl("^8", percent_ceesat_score$question), "Study Limitations", "Other"))))))

# Group by combined_category and score, then summarize
percent_ceesat_score_grouped <- percent_ceesat_score %>%
  group_by(combined_category, score) %>%
  summarise(total_n = sum(n))

# Calculate the total count for each special category
total_counts <- percent_ceesat_score_grouped %>%
  group_by(combined_category) %>%
  summarise(total_count = sum(total_n))

# Join total counts with grouped data
percent_ceesat_score_grouped <- left_join(percent_ceesat_score_grouped, total_counts, by = "combined_category")

# Add a column for relative percent
percent_ceesat_score_grouped <- percent_ceesat_score_grouped %>%
  mutate(relative_percent = (total_n / total_count) * 100)

print(percent_ceesat_score_grouped)

## # A tibble: 22 × 5
## # Groups:   combined_category [6]
##    combined_category    score total_n total_count relative_percent
##    <chr>                <fct>   <int>       <int>            <dbl>
##  1 Data Analysis        Red        23         249             9.24
##  2 Data Analysis        Amber     192         249            77.1 
##  3 Data Analysis        Green      27         249            10.8 
##  4 Data Analysis        Gold        7         249             2.81
##  5 Data Extraction      Red       184         415            44.3 
##  6 Data Extraction      Amber     130         415            31.3 
##  7 Data Extraction      Green      53         415            12.8 
##  8 Data Extraction      Gold       48         415            11.6 
##  9 Literature Screening Red       113         249            45.4 
## 10 Literature Screening Amber     120         249            48.2 
## # … with 12 more rows

# view(percent_ceesat_score_grouped)

# load data
bib_alt <- read_csv(here("data","scopus.csv")) 

# function getAltmetrics(), the only parameter is doi; see example below
getAltmetrics <- function(doi = NULL,
                          foptions = list(),
                           ...) {
    if (!is.null(doi)) doi <- stringr::str_c("doi/", doi)
    identifiers <- purrr::compact(list(doi))
    if (!is.null(identifiers)) {
      ids <- identifiers[[1]]
    }
    base_url <- "http://api.altmetric.com/v1/"
    #request <- httr::GET(paste0(base_url, ids), httr::add_headers("user-agent" = "#rstats rAltmertic package https://github.com/ropensci/rAltmetric"))
    request <- httr::GET(paste0(base_url, ids))
    results <-
      jsonlite::fromJSON(httr::content(request, as = "text"), flatten = TRUE)
    results <- rlist::list.flatten(results)
    class(results) <- "altmetric"
    results
}
altmetric.crawler <- list(NULL)
for (n in 1:length(bib_alt$DOI)) {
 # format altmetric object
  format.Altmetric <- function(altmetric.object) {
  stats <- altmetric.object[grep("^cited", names(altmetric.object))]
  stats <- data.frame(stats, stringsAsFactors = FALSE)
  data.frame(paper_title = altmetric.object$title,
             journal = altmetric.object$journal,
             doi = altmetric.object$doi,
             #subject = altmetric.object$subjects,
             Altmetric.score = altmetric.object$score,
             stats = stats)
}
   # JASON formate
  altmetric.crawler[[n]]  <-  try(list(format.Altmetric(getAltmetrics(doi = bib_alt$DOI[n])))) # https://stackoverflow.com/questions/14059657/how-to-skip-an-error-in-a-loop?rq=1
  
  # create a dataframe function
  altmetric_df <- function(altmetric.object) {
  df <- data.frame(t(unlist(altmetric.object)), stringsAsFactors = FALSE)
  }
  #altmetric.crawler[[n]]  <-  try(list(altmetric_df(getAltmetrics(doi = DOIs[n]))))
  # create a function to summarize Altmetric object
  summary.altmetric <- function(x, ...) {
  if (inherits(x, "altmetric"))  {
string <- "Altmetrics on: \"%s\" with altmetric_id: %s published in %s."
vals   <- c(x$title,  x$altmetric_id, x$journal)
 if("journal" %in% names(x)) {
  cat(do.call(sprintf, as.list(c(string, vals))))
 } else {
   string <- "Altmetrics on: \"%s\" with altmetric_id: %s"
   cat(do.call(sprintf, as.list(c(string, vals))))
 }
  cat("\n")
  stats <- x[grep("^cited", names(x))]
  stats <- data.frame(stats, stringsAsFactors = FALSE)
  print(data.frame(stats = t(stats)))
  }
}
  # crawl
 # altmetric.crawler[[n]] <- try(list(summary.altmetric(getAltmetrics(doi = DOIs[n]))))
}

## Error : lexical error: invalid char in json text.
##                                        Not Found
##                      (right here) ------^
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# save results from altmetric.crawler and retrieve lists within lists
altmetric.crawler2 <- sapply(altmetric.crawler, function(x) {x})
# retrieve stats
altmetric.summary <- data.frame(paper_title = sapply(altmetric.crawler2, function(x)  ifelse(class(x) == "data.frame",x$paper_title,NA)),
           journal = sapply(altmetric.crawler2, function(x) ifelse(class(x) == "data.frame",x$journal,NA)),
           doi = sapply(altmetric.crawler2, function(x) ifelse(class(x) == "data.frame",x$doi,NA)),
           #subject = sapply(altmetric.crawler2, function(x) ifelse(class(x) == "data.frame",x$subject,NA)),
           Altmetric.score = sapply(altmetric.crawler2, function(x) ifelse(class(x) == "data.frame",x$Altmetric.score,0)),
           policy = sapply(altmetric.crawler2, function(x) ifelse(class(x) == "data.frame",ifelse(!is.null(x$stats.cited_by_policies_count),x$stats.cited_by_policies_count,0),0)),
           patent = sapply(altmetric.crawler2, function(x) ifelse(class(x) == "data.frame",ifelse(!is.null(x$stats.cited_by_patents_count),x$stats.cited_by_patents_count,0),0))
           )
# Adding new columns to bib_alt dataframe
bib_alt <- bib_alt %>%
   mutate(Altmetric.score = altmetric.summary$Altmetric.score, 
          policy = altmetric.summary$policy, 
          patent = altmetric.summary$patent)

# Defining the scoring function
score <- function(x) {
  case_when(
    x == 'Red' ~ 0,
    x == 'Amber' ~ 1,
    x == 'Green' ~ 3,
    x == 'Gold' ~ 4,
    TRUE ~ NA_real_
  )
}
# Apply the scoring function across all columns starting with "CEESAT"
sd <- sd %>%
  mutate(across(starts_with("CEESAT"), score, .names = "points_{.col}"))
# Sum all the points columns to create a 'total_points' column
sd <- sd %>%
  mutate(total_points = rowSums(select(., starts_with("points_")), na.rm = TRUE))

sd_bib_alt <- sd %>%
  left_join(bib_alt, by = "DOI")

# Converting policy to binary and dropping NAs
sd_bib_alt_policy <- sd_bib_alt %>%
  mutate(binary_policy = if_else(policy != 0, "Cited in Policy", "Not Cited in Policy")) %>%
  drop_na(binary_policy, total_points)

# Converting policy to binary and dropping NAs
sd_bib_alt_policy <- sd_bib_alt %>%
  mutate(binary_policy = if_else(policy != 0, "Cited in Policy", "Not Cited in Policy")) %>%
  drop_na(binary_policy, total_points)
percent_ceesat_score1 <- sd_bib_alt_policy %>%
  filter(!is.na(author_year)) %>%
  select(studies = author_year, binary_policy, starts_with("CEE")) %>%
  na.omit() %>%
  pivot_longer(cols = -c(studies, binary_policy), names_to = "question", values_to = "score") %>%
  group_by(binary_policy, question, score) %>%
  summarise(n = n(), .groups = 'drop') %>%
  mutate(percent = (n/sum(n))*100, 
         across(c(question, score), as.factor),
         question = fct_recode(question, 
           `1.1` = "CEESAT2_1.1",
           `2.1` = "CEESAT2_2.1",
           `3.1` = "CEESAT2_3.1",
           `3.2` = "CEESAT2_3.2",
           `4.1` = "CEESAT2_4.1",
           `4.2` = "CEESAT2_4.2",
           `4.3` = "CEESAT2_4.3",
           `5.1` = "CEESAT2_5.1",
           `5.2` = "CEESAT2_5.2",
           `6.1` = "CEESAT2_6.1",
           `6.2` = "CEESAT2_6.2",
           `6.3` = "CEESAT2_6.3",
           `7.1` = "CEESAT2_7.1",
           `7.2` = "CEESAT2_7.2",
           `7.3` = "CEESAT2_7.3",
           `8.1` = "CEESAT2_8.1"),
         question = factor(question, levels = rev(levels(question))),
         score = factor(score, levels = levels(score)[c(4,1,3,2)]))


fig2b <- ggplot(data = percent_ceesat_score1, aes(x = question, y = percent, fill = score)) +
  geom_col(width = 0.7, position = "fill", color = "black") +
  geom_text(aes(label = n), position = position_fill(vjust = 0.5), size = 7, fontface = "bold") +
  coord_flip() +
  guides(fill = guide_legend(reverse = TRUE)) +
  scale_fill_manual(values = c("#FF0000","#FFD700","#008000", "#DAA520"), name = "Score:") +
  scale_y_continuous(labels = scales::percent) +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        panel.background = element_blank(),
        axis.text.y = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.title.x = element_text(size = 25),
        axis.title.y = element_text(size = 25), 
        legend.position = "none",
        strip.text = element_text(size = 20),
        strip.background = element_blank(), 
        plot.tag = element_text(size = 25)) + 
  ylab("Percentage") + 
  xlab("CEESAT Question") +
  facet_wrap(~binary_policy) +
  labs(tag = "B")

fig2b

# ggsave(here("figures", "fig2b.pdf"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "fig2b.jpg"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)


policy <- sd_bib_alt_policy$total_points[sd_bib_alt_policy$binary_policy == "Cited in Policy"]
not_policy <- sd_bib_alt_policy$total_points[sd_bib_alt_policy$binary_policy == "Not Cited in Policy"]

# QQ Plot
qqnorm(policy)
qqline(policy, col = "red")

# QQ Plot
qqnorm(not_policy)
qqline(not_policy, col = "red")

t.test(policy,not_policy)

## 
##  Welch Two Sample t-test
## 
## data:  policy and not_policy
## t = -0.28694, df = 74.924, p-value = 0.7749
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -4.766893  3.566553
## sample estimates:
## mean of x mean of y 
##  13.15789  13.75806

sd_bib_alt_guideline <- sd_bib_alt %>% 
  mutate(binary_guideline = if_else(reporting_standards_type == "Not reported", "Not reported", "Reported")) %>% 
    drop_na(binary_guideline, total_points)

percent_ceesat_score2 <- sd_bib_alt_guideline %>%
  filter(!is.na(author_year)) %>%
  select(studies = author_year, binary_guideline, starts_with("CEE")) %>%
  na.omit() %>%
  pivot_longer(cols = -c(studies, binary_guideline), names_to = "question", values_to = "score") %>%
  group_by(binary_guideline, question, score) %>%
  summarise(n = n(), .groups = 'drop') %>%
  mutate(percent = (n/sum(n))*100, 
         across(c(question, score), as.factor),
         question = fct_recode(question, 
           `1.1` = "CEESAT2_1.1",
           `2.1` = "CEESAT2_2.1",
           `3.1` = "CEESAT2_3.1",
           `3.2` = "CEESAT2_3.2",
           `4.1` = "CEESAT2_4.1",
           `4.2` = "CEESAT2_4.2",
           `4.3` = "CEESAT2_4.3",
           `5.1` = "CEESAT2_5.1",
           `5.2` = "CEESAT2_5.2",
           `6.1` = "CEESAT2_6.1",
           `6.2` = "CEESAT2_6.2",
           `6.3` = "CEESAT2_6.3",
           `7.1` = "CEESAT2_7.1",
           `7.2` = "CEESAT2_7.2",
           `7.3` = "CEESAT2_7.3",
           `8.1` = "CEESAT2_8.1"),
         question = factor(question, levels = rev(levels(question))),
         score = factor(score, levels = levels(score)[c(4,1,3,2)]))

figs5 <- ggplot(data = percent_ceesat_score2, aes(x = question, y = percent, fill = score)) +
  geom_col(width = 0.7, position = "fill", color = "black") +
  geom_text(aes(label = n), position = position_fill(vjust = 0.5), size = 7, fontface = "bold") +
  coord_flip() +
  guides(fill = guide_legend(reverse = TRUE)) +
  scale_fill_manual(values = c("#FF0000","#FFD700","#008000", "#DAA520"), name = "Score:") +
  scale_y_continuous(labels = scales::percent) +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        panel.background = element_blank(),
        axis.text.y = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.title.x = element_text(size = 25),
        axis.title.y = element_text(size = 25), 
        legend.position = "top",  
        legend.justification = "left",  
        legend.direction = "horizontal",
        legend.box = "horizontal",  
        legend.box.just = "left",  
        legend.title.align = 0.5,  
        legend.key.size = unit(1, "cm"),  
        legend.title = element_text(size =25),
        legend.text = element_text(size = 25),  
        strip.text = element_text(size = 20),
        strip.background = element_blank()) +  
  ylab("Percentage") + 
  xlab("CEESAT Question") +
  facet_wrap(~binary_guideline) 

figs5

# ggsave(here("figures", "figs5.pdf"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs5.jpg"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)

guidline <- sd_bib_alt_guideline$total_points[sd_bib_alt_guideline$binary_guideline == "Reported"]
no_guideline <- sd_bib_alt_guideline$total_points[sd_bib_alt_guideline$binary_guideline == "Not reported"]

# QQ Plot
 qqnorm(guidline)
 qqline(guidline, col = "red")

# QQ Plot
 qqnorm(no_guideline)
 qqline(no_guideline, col = "red")

t.test(guidline,no_guideline)

## 
##  Welch Two Sample t-test
## 
## data:  guidline and no_guideline
## t = 6.1128, df = 68.538, p-value = 5.269e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   6.091412 11.994595
## sample estimates:
## mean of x mean of y 
##  21.92105  12.87805

# Create the plot 
figs6 <- sd_bib_alt_policy %>%
 # filter(total_points != 0, policy != 0) %>% 
  ggplot(aes(x = total_points, y = policy)) +
  geom_point(color = "#1b9e77", alpha = 0.8, size = 8) +
  geom_smooth(method = lm, se = TRUE, color = "red") +
  labs(x = "Total CEESAT score", y = "Policy Count") +
  theme_minimal() +
  theme(panel.grid.major.y = element_blank(),
        axis.line.y = element_blank(),
        axis.ticks.y = element_blank(),
        axis.text.x = element_text(size = 25),
        axis.text.y = element_text(size = 25),
        axis.title.x = element_text(size = 30),
        axis.title.y = element_text(size = 30),
        plot.title = element_blank())

figs6

# ggsave(here("figures", "figs6.pdf"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs6.jpg"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)

figs7 <- sd_bib_alt_policy %>%
  ggplot(aes(x = binary_policy, y = total_points)) +
  geom_violin(fill = "#1b9e77", alpha =0.2, color = NA, trim = FALSE) +
  geom_boxplot(width = 0.05, fill = "white", color = "#1b9e77", outlier.shape = NA) +
  geom_jitter(width = 0.1, height = 0.05, color = "#1b9e77", alpha = 0.8, size = 6) +
  scale_y_continuous(limits = c(0, max(sd_bib_alt$total_points))) +
  labs( x = "Policy", y = "Total CEESAT Score") +
  theme_minimal() +
  theme(panel.grid.major.y = element_blank(),
        axis.line.y = element_blank(),
        axis.ticks.y = element_blank(),
        axis.text.x = element_text(size = 25),
        axis.text.y = element_text(size = 25),
        axis.title.x = element_text(size = 30),
        axis.title.y = element_text(size = 30),
        plot.title = element_blank())

figs7

# ggsave(here("figures", "figs7.pdf"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs7.jpg"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)

# Calculate the total count for each category
bias_method_count <- sd %>% 
  separate_rows(bias_assessment_method, sep = ",\\s+") %>% 
  count(bias_assessment_method) %>%
  filter(bias_assessment_method != "NA") %>%
    group_by(bias_assessment_method) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
bias_method_pct <- bias_method_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
fig3a <- bias_method_count %>%
  ggplot(aes(x = n, y = reorder(bias_assessment_method, n), fill = ifelse(grepl("Not reported", bias_assessment_method), "#7370b3", "#1b9e77"))) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(bias_assessment_method, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = bias_method_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(bias_method_count$n)*1.3)) +
  labs(title = "Publication Bias Count", x = NULL, y = NULL, tag = "A", size = 30) +
  organochlorTHEME() 

fig3a

# ggsave(here("figures", "fig3h.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "fig3h.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Data Transformation 
bias_assessment_alluvial <- sd %>% 
    separate_rows(bias_assessment_method, sep = ",\\s+") %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>% 
    filter(!is.na(bias_assessment_method)) %>%
    group_by(Journal_Category_Allocated_Broad, bias_assessment_method) %>% 
    count(bias_assessment_method, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(bias_assessment_method)

# Create the Alluvial plot
figs8 <- bias_assessment_alluvial %>% 
  ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = bias_assessment_method)) +
  scale_x_discrete(limits = c("Journal Citation Category", "Bias Assessment"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/3.5, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 6) +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs8

# ggsave(here("figures", "figs8.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs8.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Calculate the total count for each category
heterogeneity_count <- sd %>% 
  separate_rows(heterogeneity_assessment_method, sep = ",\\s+") %>% 
  count(heterogeneity_assessment_method) %>%
  filter(heterogeneity_assessment_method != "NA") %>%
    group_by(heterogeneity_assessment_method) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
heterogeneity_pct <- heterogeneity_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
fig3b <- heterogeneity_count %>%
  ggplot(aes(x = n, y = reorder(heterogeneity_assessment_method, n), fill = ifelse(grepl("Not reported", heterogeneity_assessment_method), "#7370b3", "#1b9e77"))) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(heterogeneity_assessment_method, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = heterogeneity_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(heterogeneity_count$n)*1.3)) +
  labs(title = "Heterogeneity Count", x = NULL, y = NULL, tag = "B", size = 30) +
  organochlorTHEME() 

fig3b

# ggsave(here("figures", "fig3b.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "fig3b.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Data Transformation 
heterogeneity_alluvial <- sd %>% 
    separate_rows(heterogeneity_assessment_method, sep = ",\\s+") %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>% 
    filter(!is.na(heterogeneity_assessment_method)) %>%
    group_by(Journal_Category_Allocated_Broad, heterogeneity_assessment_method) %>% 
    count(heterogeneity_assessment_method, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(heterogeneity_assessment_method) 

# Create the Alluvial plot
figs9 <- heterogeneity_alluvial %>% 
ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = heterogeneity_assessment_method)) +
  scale_x_discrete(limits = c("Journal Citation Category", "Heterogeneity"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/4.5, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 6) +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs9

# ggsave(here("figures", "figs9.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs9.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Calculate the total count for each category
sensitivity_count <- sd %>% 
  separate_rows(sensitivity_analysis_method, sep = ",\\s+") %>% 
  count(sensitivity_analysis_method) %>%
  filter(sensitivity_analysis_method != "NA") %>%   
  group_by(sensitivity_analysis_method) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
sensitivity_pct <- sensitivity_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
fig3c <- sensitivity_count %>%
  ggplot(aes(x = n, y = reorder(sensitivity_analysis_method, n), fill = ifelse(grepl("Not reported", sensitivity_analysis_method), "#7370b3", "#1b9e77"))) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(sensitivity_analysis_method, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = sensitivity_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(sensitivity_count$n)*1.3)) +
  labs(title = "Sensitivity Analysis Count", x = NULL, y = NULL, tag = "C", size = 30) +
  organochlorTHEME() 

fig3c

# ggsave(here("figures", "fig3c.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "fig3c.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Data Transformation 
sensitivity_analysis_alluvial <- sd %>% 
    separate_rows(sensitivity_analysis_method, sep = ",\\s+") %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>% 
    filter(!is.na(sensitivity_analysis_method)) %>%
    group_by(Journal_Category_Allocated_Broad, sensitivity_analysis_method) %>% 
    count(sensitivity_analysis_method, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(sensitivity_analysis_method)

# Create the Alluvial plot
figs10 <- sensitivity_analysis_alluvial %>% 
ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = sensitivity_analysis_method)) +
  scale_x_discrete(limits = c("Journal Citation Category", "Sensitivity Analysis"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/4, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 5) +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs10

# ggsave(here("figures", "figs10.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs10.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Calculate the total count for each category
reporting_guide_count <- sd %>% 
  separate_rows(reporting_standards_type, sep = ",\\s+") %>% 
  count(reporting_standards_type) %>%
  filter(reporting_standards_type != "NA") %>%
  mutate(reporting_standards_type = ifelse(n < 3, "Other Guidelines", as.character(reporting_standards_type))) %>%
  group_by(reporting_standards_type) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
reporting_guide_pct <- reporting_guide_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
fig3d <- reporting_guide_count %>%
  ggplot(aes(x = n, y = reorder(reporting_standards_type, n), fill = ifelse(grepl("Not reported", reporting_standards_type), "#7370b3", "#1b9e77"))) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(reporting_standards_type, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = reporting_guide_pct, aes(label = paste0("(", round(percentage, 0), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
 scale_x_continuous(expand = c(0, 0), limits = c(0, max(reporting_guide_count$n)*1.3)) +
  labs(title = "Reporting Guideline Count", x = NULL, y = NULL, size = 30) +
  organochlorTHEME() 

fig3d

# ggsave(here("figures", "fig3d.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "fig3d.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Data Transformation 
reporting_standards_alluvial <- sd %>% 
    separate_rows(reporting_standards_type, sep = ",\\s+") %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>%
    filter(!grepl("no category found", Journal_Category_Allocated_Broad, ignore.case = TRUE)) %>% 
    filter(!is.na(reporting_standards_type)) %>%
    group_by(Journal_Category_Allocated_Broad, reporting_standards_type) %>% 
    count(reporting_standards_type, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(reporting_standards_type)

# Create the Alluvial plot
figs11 <- reporting_standards_alluvial %>% 
ggplot( aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = reporting_standards_type)) +
  scale_x_discrete(limits = c("Journal Citation Category", "Reporting Standards Type"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/3.5, fill = "white", color = "black") +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 6) +
    scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs11

# ggsave(here("figures", "figs11.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs11.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

fig3 <- fig3a + fig3b + fig3c + fig3d +
        plot_layout(ncol = 2, nrow = 2)

## Error in eval(expr, envir, enclos): object 'fig3c' not found

fig3

## Error in eval(expr, envir, enclos): object 'fig3' not found

# ggsave(here("figures", "fig3.pdf"), width = 36 , height = 20 , units = "cm", scale = 2, dpi = 400)
# ggsave(here("figures", "fig3.jpg"), width = 36 , height = 20, units = "cm", scale = 2, dpi = 400)

# Calculate the total count for each category
database_count <- sd %>% 
  separate_rows(database_search, sep = ",\\s+") %>% 
  count(database_search) %>% 
  filter(database_search != "NA") %>% 
  arrange(desc(n)) %>% 
  mutate(database_search = ifelse(n<= 3, "Other databases", as.character(database_search))) %>% 
    group_by(database_search) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
database_pct <- database_count %>%
  mutate(proportion = n / 83, # 83 is number of studies assessed for CEESAT
         percentage = proportion * 100)

# Create the count plot
figs12 <- database_count %>%
  ggplot(aes(x = n, y = reorder(database_search, n), fill = "#1b9e77")) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(database_search, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = database_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(database_count$n)*1.3)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME() 

figs12

# ggsave(here("figures", "figs12.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs12.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Rename Environmental Science
sd <- sd %>%
  mutate(Journal_Category_Allocated_Broad = str_replace(Journal_Category_Allocated_Broad, "Environmental Science", "Environmental\nScience"))

# Data Transformation 
database_alluvial <- sd %>% 
    separate_rows(database_search, sep = ",\\s+") %>%
    group_by(Journal_Category_Allocated_Broad, database_search) %>% 
    count(database_search, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(database_search) %>% 
    filter(sum(freq) >= 3) %>% 
    filter(database_search != "NA")

# Create the Alluvial plot
figs13 <- database_alluvial %>% 
ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = database_search)) +
  scale_x_discrete(limits = c("Journal Citation Report", "Database Search"), expand = c(.05, .05)) +
  xlab("Variables") +
  ylab("Frequency") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/4.5, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 6) +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs13

# ggsave(here("figures", "figs13.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs13.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Calculate the total count for each category
software_count <- sd %>%
  separate_rows(software_analysis, sep = ",\\s+") %>%
  mutate(software_analysis = ifelse(grepl("comprehensive meta-analysis", software_analysis), "CMAS", software_analysis)) %>%
  count(software_analysis) %>%
  filter(software_analysis != "NA") %>%
  arrange(desc(n)) %>%
  mutate(software_analysis = ifelse(n < 2, "Other Software", as.character(software_analysis))) %>%
  group_by(software_analysis) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
software_pct <- software_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
figs14 <- software_count %>%
  ggplot(aes(x = n, y = reorder(software_analysis, n), fill = "#1b9e77")) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(software_analysis, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = software_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(software_count$n)*1.3)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME() 

figs14

# ggsave(here("figures", "figs14.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs14.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Data Transformation 
software_analysis_alluvial <- sd %>% 
    separate_rows(software_analysis, sep = ",\\s+") %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>% 
  mutate(software_analysis = ifelse(grepl("comprehensive meta-analysis", software_analysis), "CMAS", software_analysis)) %>%
  mutate(software_analysis = ifelse(grepl("not reported", software_analysis), "no software reported", software_analysis))  %>% 
    filter(!is.na(software_analysis)) %>%
    group_by(Journal_Category_Allocated_Broad, software_analysis) %>% 
    count(software_analysis, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(software_analysis)

# Create the Alluvial plot for Software Analysis
figs15 <- software_analysis_alluvial %>% 
  ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = software_analysis)) +
  scale_x_discrete(limits = c("Journal Citation Category", "Software Analysis"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/4, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 6) +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs15

# ggsave(here("figures", "figs15.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs15.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Calculate the total count for each category
effectsize_count <- sd %>% 
  separate_rows(effect_size, sep = ",\\s+") %>% 
  count(effect_size) %>%
  filter(effect_size != "NA") %>%
  mutate(effect_size = ifelse(n<= 2, "Other effect size", as.character(effect_size))) %>% 
  group_by(effect_size) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
effectsize_pct <- effectsize_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
figs16 <-  effectsize_count %>%
  ggplot(aes(x = n, y = reorder(effect_size, n), fill = "#1b9e77")) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(effect_size, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = effectsize_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(effectsize_count$n)*1.3)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME() 

figs16

# ggsave(here("figures", "figs16.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs16.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Data Transformation 
effectsize_alluvial <- sd %>% 
    separate_rows(effect_size, sep = ",\\s+") %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>% 
    filter(!grepl("no category found", Journal_Category_Allocated_Broad, ignore.case = TRUE)) %>% 
    filter(!is.na(effect_size)) %>%
    group_by(Journal_Category_Allocated_Broad, effect_size) %>% 
    count(effect_size, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(effect_size) 


# Create the Alluvial plot
figs17 <- effectsize_alluvial %>% 
ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = effect_size)) +
  scale_x_discrete(limits = c("Journal Citation Category", "Effect Size"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/2.5, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 5) +
  theme_minimal() +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 
  
figs17

# ggsave(here("figures", "figs17.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs17.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Calculate the total count for each category
visualization_count <- sd %>% 
  separate_rows(visualization_method, sep = ",\\s+") %>% 
  count(visualization_method) %>%
  filter(visualization_method != "NA") %>%
    group_by(visualization_method) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
visualization_pct <- visualization_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
figs18 <- visualization_count %>%
  ggplot(aes(x = n, y = reorder(visualization_method, n), fill = ifelse(grepl("Not reported", visualization_method), "#7370b3", "#1b9e77"))) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(visualization_method, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = visualization_pct, aes(label = paste0("(", round(percentage, 0), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(visualization_count$n)*1.3)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME() 

figs18

# ggsave(here("figures", "figs18.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs18.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Data Transformation 
visualization_alluvial <- sd %>% 
    separate_rows(visualization_method, sep = ",\\s+") %>%
    filter(!is.na(visualization_method)) %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>%
    group_by(Journal_Category_Allocated_Broad, visualization_method) %>% 
    count(visualization_method, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(visualization_method) 


# Create the Alluvial plot
figs19 <- visualization_alluvial %>% 
ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = visualization_method)) +
  scale_x_discrete(limits = c("Journal Citation Report Category", "ROB Assessment Method"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/4, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 6) +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs19

# ggsave(here("figures", "figs19.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs19.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Calculate the total count for each category
rob_method_count <- sd %>% 
  separate_rows(rob_assessment_method, sep = ",\\s+") %>% 
  count(rob_assessment_method) %>%
  filter(rob_assessment_method != "NA") %>%
  mutate(rob_assessment_method = ifelse(n<= 2, "Other ROB tool", as.character(rob_assessment_method))) %>% 
  mutate(rob_assessment_method = ifelse(grepl("Newcastle Ottawa scale", rob_assessment_method), "Newcastle Ottawa\nscale", rob_assessment_method)) %>%
  group_by(rob_assessment_method) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
rob_method_pct <- rob_method_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
figs20 <- rob_method_count %>%
  ggplot(aes(x = n, y = reorder(rob_assessment_method, n), fill = ifelse(grepl("Not reported", rob_assessment_method), "#7370b3", "#1b9e77"))) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(rob_assessment_method, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = rob_method_pct, aes(label = paste0("(", round(percentage, 0), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(rob_method_count$n)*1.3)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME() 

figs20

# ggsave(here("figures", "figs20.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs20.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Data Transformation 
robmethod_alluvial <- sd %>% 
     separate_rows(rob_assessment_method, sep = ",\\s+") %>%
    filter(!is.na(rob_assessment_method)) %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>%
    group_by(Journal_Category_Allocated_Broad, rob_assessment_method) %>% 
    count(rob_assessment_method, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(rob_assessment_method) 


# Create the Alluvial plot
figs21 <- robmethod_alluvial %>% 
ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = rob_assessment_method)) +
  scale_x_discrete(limits = c("Journal Citation Category", "ROB Assessment Method"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/3, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 5) +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs21

# ggsave(here("figures", "figs21.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs21.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Calculate the total count for each category
bias_visualization_count <- sd %>% 
  separate_rows(bias_assessment_visualization, sep = ",\\s+") %>% 
  count(bias_assessment_visualization) %>%
  filter(bias_assessment_visualization != "NA") %>%
    group_by(bias_assessment_visualization) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
bias_visualization_pct <- bias_visualization_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
figs22 <- bias_visualization_count %>%
  ggplot(aes(x = n, y = reorder(bias_assessment_visualization, n), fill = ifelse(grepl("Not reported", bias_assessment_visualization), "#7370b3", "#1b9e77"))) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(bias_assessment_visualization, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = bias_visualization_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(bias_visualization_count$n)*1.3)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME() 

figs22

# ggsave(here("figures", "figs22.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs22.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Data Transformation 
bias_vizualisation_alluvial <- sd %>% 
    separate_rows(bias_assessment_visualization, sep = ",\\s+") %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>% 
    filter(!is.na(bias_assessment_visualization)) %>%
    group_by(Journal_Category_Allocated_Broad, bias_assessment_visualization) %>% 
    count(bias_assessment_visualization, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(bias_assessment_visualization)

# Create the Alluvial plot
figs23 <- bias_vizualisation_alluvial %>% 
  ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = bias_assessment_visualization)) +
  scale_x_discrete(limits = c("Journal Citation Category", "Bias Vizualization"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/4, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 6) +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs23

# ggsave(here("figures", "figs23.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs23.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Grouping "Medline" and "Pubmed" under "PubMed" and summing the counts
database_count <- database_count %>%
  mutate(database_search = if_else(database_search %in% c("Medline", "Pubmed"), "PubMed", database_search)) %>%
  group_by(database_search) %>%
  summarise(n = sum(n))

# Splitting, grouping, and summing different categories of effect sizes
effectsize_count <- sd %>% 
  separate_rows(effect_size, sep = ",\\s+") %>% 
  count(effect_size) %>%
  filter(effect_size != "NA") %>%
  group_by(effect_size) %>%
  summarise(n = sum(n))

effectsize_count <- effectsize_count %>% 
  mutate(effect_size = if_else(effect_size %in% c("Beta regression", "Correlation coefficient"), "Correlation", effect_size)) %>% 
  mutate(effect_size = if_else(effect_size %in% c("Odds ratio", "Response ratio", "Standardized mean difference", "Log partitioning ratio", "Log odds ratio", "Ratio of means", "Log response ratio"), "Mean difference", effect_size)) %>% 
  mutate(effect_size = if_else(effect_size %in% c("Risk ratio"), "2x2", effect_size)) %>% 
  mutate(effect_size = if_else(effect_size %in% c("Raw weight", "Geometric mean", "Maternal transfer ratio", "Standardized mortality rate", "Transfer rate","Transfer ratio", "Z-score", "Log coefficient variation ratio"), "Other ES", effect_size)) %>% 
  group_by(effect_size) %>%
  summarise(n = sum(n))

# Categorizing software as either "Code-based software" or "GUI" and summing the counts
software_count <- software_count %>%
  mutate(software_analysis = if_else(software_analysis %in% c("Stata", "R"), "Code-based", software_analysis)) %>%
  mutate(software_analysis = if_else(software_analysis %in% c("CMAS", "Excel", "RevMan", "SAS", "XLSTAT"), "GUI", software_analysis)) %>% 
  mutate(software_analysis = if_else(software_analysis %in% c("Not reported"), "No software reported", software_analysis)) %>%
  group_by(software_analysis) %>%
  summarise(n = sum(n))

# Grouping heterogeneity assessment methods and summing the counts
heterogeneity_count <- heterogeneity_count %>% 
  mutate(heterogeneity_assessment_method = if_else(heterogeneity_assessment_method %in% c("Tau square"), "I squared", heterogeneity_assessment_method)) %>% 
  mutate(heterogeneity_assessment_method = if_else(heterogeneity_assessment_method %in% c("Chi square"), "Q statistic", heterogeneity_assessment_method)) %>% 
  mutate(heterogeneity_assessment_method = if_else(heterogeneity_assessment_method %in% c("Not reported"), "No heterogeneity reported", heterogeneity_assessment_method)) %>% 
  group_by(heterogeneity_assessment_method) %>%
  summarise(n = sum(n))

# Grouping bias assessment methods with count <= 3 under "Other BA" and summing the counts
bias_method_count <- bias_method_count %>% 
  mutate(bias_assessment_method = if_else(bias_assessment_method %in% c("Egger's regression test", "Fail safe", "Begg's test", "Kendall's tau statistic"),"Bias statistical test", bias_assessment_method)) %>% 
    mutate(bias_assessment_method = if_else(bias_assessment_method %in% c("Not reported"),"No bias assessment reported", bias_assessment_method)) %>% 
  group_by(bias_assessment_method) %>%
  summarise(n = sum(n))

# Grouping sensitivity analysis methods and summing the counts
sensitivity_count <-  sensitivity_count %>%  
  mutate(sensitivity_analysis_method = if_else(n<= 8, "Other sensitivity analysis", as.character(sensitivity_analysis_method))) %>% 
   mutate(sensitivity_analysis_method = if_else(sensitivity_analysis_method %in% c("Not reported"), "No sensitivity analysis reported",sensitivity_analysis_method)) %>% 
  group_by(sensitivity_analysis_method) %>%
  summarise(n = sum(n))

# Grouping risk of bias assessment methods with count <= 3 under "Other ROB" and summing the counts
rob_method_count <-  rob_method_count %>%  
  mutate(rob_assessment_method = if_else(n<= 3, "Other ROB tool", as.character(rob_assessment_method))) %>% 
    mutate(rob_assessment_method = if_else(rob_assessment_method %in% c("Not reported"), "No risk of bias reported", rob_assessment_method)) %>% 
  group_by(rob_assessment_method) %>%
  summarise(n = sum(n))

# Grouping visualization methods with count <= 3 under "Other Viz" and summing the counts
visualization_count <- visualization_count %>% 
   mutate(visualization_method = if_else(n<= 3, "Other Viz", as.character(visualization_method))) %>% 
  mutate(visualization_method = if_else(visualization_method %in% c("Not reported"), "No vizualization reported", visualization_method)) %>% 
  group_by(visualization_method) %>%
  summarise(n = sum(n))

# Grouping reporting standards types with count <= 2 under "Other guideline" and summing the counts
reporting_guide_count <- reporting_guide_count %>% 
  mutate(reporting_standards_type = ifelse(n<= 2, "Other guideline", as.character(reporting_standards_type))) %>% 
  mutate(reporting_standards_type = ifelse(reporting_standards_type %in% c("Not reported"), "No reporting guideline reported",reporting_standards_type)) %>% 
  group_by(reporting_standards_type) %>%
  summarise(n = sum(n))

# Grouping bias assessment visualization types and summing the counts
bias_visualization_count <- bias_visualization_count %>% 
  mutate(bias_assessment_visualization = if_else(bias_assessment_visualization %in% c("Doi plot", "Trim and fill", "Funnel plot", "Galbraith plot"), "Bias visualization", bias_assessment_visualization)) %>% 
  mutate(bias_assessment_visualization = if_else(bias_assessment_visualization %in% c("Not reported"), "No bias visualization reported", bias_assessment_visualization)) %>% 
  group_by(bias_assessment_visualization) %>%
  summarise(n = sum(n))

# Combine the data frames and unite the methodology types
df <- bind_rows(
  database_count %>% mutate(methodology_type = 'Database Search'),
  effectsize_count %>% mutate(methodology_type = 'Effect Size'),
  software_count %>%  mutate(methodology_type = "Software"),
  heterogeneity_count %>% mutate(methodology_type = "Heterogeneity"),
  sensitivity_count %>% mutate(methodology_type = "Sensitivity_Analysis"),
  bias_method_count %>%  mutate(methodology_type = "Bias Assessment"),
  rob_method_count %>% mutate(methodology_type = "Risk  of Bias"), 
 # visualization_count %>% mutate(methodology_type = "Visualization"),
  reporting_guide_count %>%  mutate(methodology_type = "Reporting Guide"),
  bias_visualization_count %>%  mutate(methodology_type = "Bias Assessment")
) %>% 
 unite(methodology_type_specific, 
       database_search, 
       effect_size,  
       software_analysis,
       heterogeneity_assessment_method, 
       sensitivity_analysis_method, 
       bias_assessment_method,
       rob_assessment_method, 
      # visualization_method, 
       reporting_standards_type, 
       bias_assessment_visualization,
       remove = TRUE, na.rm = TRUE)

# Preparing the edges dataframe for creating the graph
edges <- df %>%
  rename(from = methodology_type, to = methodology_type_specific, size = n) %>%
  select(c(from,to,size)) %>%
  as.data.frame()

# Preparing the vertices dataframe for creating the graph
vertices <- df  %>%
  rename(name = methodology_type_specific, size = n) %>%
  select(c(name,size)) %>%
  as.data.frame()

# Appending unique 'from' values to vertices and their corresponding summed sizes
vertices[(nrow(edges)+1):(nrow(edges)+length(unique(edges$from))),1] <- unique(edges$from)
N <- aggregate(edges$size, list(edges$from), FUN=sum)
vertices[(nrow(edges)+1):(nrow(edges)+length(unique(edges$from))),2] <- N$x

# Creating a graph object from the edges and vertices dataframes
mygraph <- graph_from_data_frame(edges, vertices = vertices)

# Plotting the graph using a 'circlepack' layout, adding labels and adjusting aesthetics

figs24 <- ggraph(mygraph, layout = 'circlepack', weight = size) + 
    geom_node_circle(aes(fill = as.factor(depth)), color = NA) +
    scale_fill_manual(values = c("#1b9e77", "#88D1AD")) + 
    geom_node_text(aes(label = name, filter = leaf, size = 10), vjust = -0.3, fontface = "bold") +
    geom_node_text(aes(label = paste0("(", size, ")"), filter = leaf, size = 10), vjust = 1, fontface = "bold") +
    theme_void() +
    theme(legend.position = "none") 

figs24

ggsave(here("figures", "figs24.pdf"), width = 18, height = 15, units = "cm", scale = 2, dpi = 800)
ggsave(here("figures", "figs24.jpg"), width = 18, height = 15, units = "cm", scale = 2, dpi = 800)

# Function to replace long OCP names with abbreviations
replace_ocp <- function(df) {
  df %>% 
    mutate(ocp = case_when(
      # HCH related replacements
      grepl("Hexachlorocyclohexane \\(HCH\\)", ocp, ignore.case = TRUE) ~ "HCH",
      grepl("alpha-Hexachlorocyclohexane \\(alpha-HCH\\)", ocp, ignore.case = TRUE) ~ "α - HCH",
      grepl("beta-Hexachlorocyclohexane \\(beta-HCH\\)", ocp, ignore.case = TRUE) ~ "β - HCH",
      grepl("gamma-Hexachlorocyclohexane \\(gamma-HCH\\)", ocp, ignore.case = TRUE) ~ "Lindane",
      # DDT related replacements
      grepl("Dichlorodiphenyltrichloroethane \\(DDT\\)", ocp, ignore.case = TRUE) ~ "DDT",
      grepl("p,p-Dichlorodiphenyltrichloroethane \\(p,p-DDT\\)", ocp, ignore.case = TRUE) ~ "p,p-DDT",
      grepl("o,p-Dichlorodiphenyltrichloroethane \\(o,p-DDT\\)", ocp, ignore.case = TRUE) ~ "o,p-DDT",
      # DDD related replacements
      grepl("Dichlorodiphenyldichloroethane \\(DDD\\)", ocp, ignore.case = TRUE) ~ "DDD",
      grepl("p,p-Dichlorodiphenyldichloroethane \\(p,p-DDD\\)", ocp, ignore.case = TRUE) ~ "p,p-DDD",
      grepl("o,p-Dichlorodiphenyldichloroethane \\(o,p-DDD\\)", ocp, ignore.case = TRUE) ~ "o,p-DDD",
      # DDE related replacements
      grepl("Dichlorodiphenyldichloroethylene \\(DDE\\)", ocp, ignore.case = TRUE) ~ "DDE",
      grepl("p,p-Dichlorodiphenyldichloroethylene \\(p,p-DDE\\)", ocp, ignore.case = TRUE) ~ "p,p-DDE",
      grepl("o,p-Dichlorodiphenyldichloroethylene \\(o,p-DDE\\)", ocp, ignore.case = TRUE) ~ "o,p-DDE",
      TRUE ~ ocp  # no change for any others
    ))
}
# Transform the data 
ocp_count <-
  ocp %>% 
  separate_rows(ocp, sep = ",\\s+") %>% 
  replace_ocp %>% 
  count(ocp) %>% 
  filter(!is.na(ocp)) %>% # filter out NA 
  arrange(desc(n)) %>% 
  mutate(ocp = ifelse(n <= 6, "other OCP", as.character(ocp))) %>% 
# Other organochlorine pesticides include: toxaphene (n=6), Methoxychlor (n=6), p,p-DDD (n=5), o,p-DDD (n=5), cis-Chlordane (n=5), trans=Chlordane (n=4), Endosulfan II (n=4), Endosulfan (n=3),o,p-DDD (n=3), cis-Nonachlor (n=3) y-Chlordane (n=2), Chlorophenol (n=2), beta-BHC (n=2), alpha-Chlordane (n=2), TCDD (n=1), Nonachlore (n=1), Endrin ketone (n=1), Endrin Aldehyde (n=1), Endosulfan sulfate (n=1), Dicofol (n=1), delta-HCH (n=1), delta-BHC (n=1), DDD (n=1), cis-Heptachlor (n=1), alpha-BHC (n=1).
  group_by(ocp) %>% 
  summarise(n =sum(n))

# Calculate the proportion and percentage of each OCP
ocp_pct <- ocp_count %>%
  mutate(proportion = n/104,
         percentage = proportion*100)


# Create the count plot for OCPs
figs25 <- ocp_count %>%
  ggplot(aes(x = n, y = reorder(ocp, n), fill = "#1b9e77")) +
  geom_bar(stat = "identity", width = 0.8, alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(ocp, n)), hjust = 0.5, size = 7, color = "black") +
  geom_text(data = ocp_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = - 0.1, size = 7, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(name = "Article Count", expand = c(0, 0), limits = c(0, max(ocp_count$n)*1.2)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME() +
  theme(axis.text.y = element_text(size = 20))  

figs25

# ggsave(here("figures", "figs25.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs25.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Calculate total count for each category
subject_count <- sub %>% 
  separate_rows(subject, sep = ",\\s+") %>% 
  mutate(subject = ifelse(grepl("Non-human animal", subject, ignore.case = TRUE), "Non-human\nanimal", subject)) %>% 
  count(subject)
   

# Calculate proportion and percentage for each category
subject_pct <- subject_count %>%
  mutate(proportion = n/105,
         percentage = proportion*100)

# Create the count plot for subjects
figs26 <- subject_count %>%
  ggplot(aes(x = n, y = reorder(subject, n), fill = "#1b9e77")) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(subject, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = subject_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(name = "Article Count", expand = c(0, 0), limits = c(0, max(subject_count$n)*1.2)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME()   


figs26

# ggsave(here("figures", "figs26.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs26.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Calculate total count for each category
impact_count <- 
  im %>% 
    separate_rows(impact, sep = ",\\s+") %>% 
    count(impact) %>% 
  filter(impact != "NA") %>%
  mutate(impact = ifelse(n<= 2, "other", as.character(impact))) %>% 
    group_by(impact) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
impact_pct <- impact_count %>%
  mutate(proportion = n/sum(impact_count$n),
         percentage = proportion*100)

# Create the count plot for impacts 
figs27 <- impact_count %>%
  ggplot(aes(x = n, y = reorder(impact, n), fill = "#1b9e77")) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(impact, n)), hjust = 0.5, size = 10, color = "black") +
  geom_text(data = impact_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 10, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(name = "Article Count", expand = c(0, 0), limits = c(0, max(impact_count$n)*1.3)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME() 

figs27

# ggsave(here("figures", "figs27.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs27.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Create a collumn for broad impacts
im <- im %>%
        separate_rows(impact, sep = ",\\s+") %>% 
  mutate(impact_broad = case_when(
    impact %in% c("parkinsons disease", "alzheimers disease", "autism spectrum disorder", "brain tumour", "amyotrophic lateral sclerosis" ) ~ "Neurological",
    impact %in% c("concentration", "contamination") ~ "Concentration",
    impact %in% c("diabetes", "thyroid function", "hypertension", "endometriosis") ~ "Endocrine",
    grepl("cancer", impact, ignore.case = TRUE) | impact %in% c("leukemia", "lymphoma", "multiple myeloma", "neuroblastoma") ~ "Carcinogen",
    impact %in% c("respiratory health", "cardiovascular disease", "asthma", "prolonged bradycardia") ~ "Cardiovascular",
 #   impact %in% c("birth outcomes", "birth weight", "preterm birth") ~ "Birth",
    impact %in% c("obesity", "adiposity") ~ "Obesity",
    impact %in% c("sperm quality", "neuroblastoma", "hypospadias", "cryptochidism", "reproductive system") ~ "Reproduction",
    TRUE ~ "Other Impact"
  ))


# Calculate total count for each category
impact_count_broad <- 
  im %>% 
    separate_rows(impact_broad, sep = ",\\s+") %>% 
    count(impact_broad) %>% 
  filter(impact_broad != "NA") %>%
    group_by(impact_broad) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
impact_pct_broad <- impact_count_broad %>%
  mutate(proportion = n/105,
         percentage = proportion*100)


# Create the count plot for impacts 
figs28 <- impact_count_broad %>%
  ggplot(aes(x = n, y = reorder(impact_broad, n), fill = "#1b9e77")) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(impact_broad, n)), hjust = 0.5, size = 10, color = "black") +
  geom_text(data = impact_pct_broad, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 10, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(name = "Article Count", expand = c(0, 0), limits = c(0, max(impact_count_broad$n)*1.3)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME()

figs28

# ggsave(here("figures", "figs28.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs28.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

# Function to change OCP to be more general
replace_ocp2 <- function(df) {
  df %>% 
  mutate(ocp = case_when(
    grepl("Chlordane", ocp, ignore.case = TRUE) ~ "Chlordane",  
    grepl("Endosulfan", ocp, ignore.case = TRUE) ~ "Endosulfan", 
    grepl("Nonachlor", ocp, ignore.case = TRUE) ~ "Nonachlore", 
    grepl("Heptachlor", ocp, ignore.case = TRUE) ~ "Heptachlor",
    grepl("TCDD", ocp, ignore.case = TRUE) ~ "TCDD", 
    grepl("Endrin", ocp, ignore.case = TRUE) ~ "Endrin",
    grepl("Hexachlorobenzene", ocp, ignore.case = TRUE) ~ "HCH",
    grepl("Lindane", ocp, ignore.case = TRUE) ~ "HCH", 
    grepl("Hexachlorocyclohexane \\(HCH\\)", ocp, ignore.case = TRUE) ~ "HCH",
    grepl("alpha-Hexachlorocyclohexane \\(alpha-HCH\\)", ocp, ignore.case = TRUE) ~ "HCH",
    grepl("beta-Hexachlorocyclohexane \\(beta-HCH\\)", ocp, ignore.case = TRUE) ~ "HCH",
    grepl("gamma-Hexachlorocyclohexane \\(gamma-HCH\\)", ocp, ignore.case = TRUE) ~ "HCH",
    grepl("Dichlorodiphenyltrichloroethane \\(DDT\\)", ocp, ignore.case = TRUE) ~ "DDT",
    grepl("p,p-Dichlorodiphenyltrichloroethane \\(p,p-DDT\\)", ocp, ignore.case = TRUE) ~ "DDT",
    grepl("o,p-Dichlorodiphenyltrichloroethane \\(o,p-DDT\\)", ocp, ignore.case = TRUE) ~ "DDT",
    grepl("Dichlorodiphenyldichloroethane \\(DDD\\)", ocp, ignore.case = TRUE) ~ "DDD",
    grepl("p,p-Dichlorodiphenyldichloroethane \\(p,p-DDD\\)", ocp, ignore.case = TRUE) ~ "DDD",
    grepl("o,p-Dichlorodiphenyldichloroethane \\(o,p-DDD\\)", ocp, ignore.case = TRUE) ~ "DDD",
    grepl("Dichlorodiphenyldichloroethylene \\(DDE\\)", ocp, ignore.case = TRUE) ~ "DDE",
    grepl("p,p-Dichlorodiphenyldichloroethylene \\(p,p-DDE\\)", ocp, ignore.case = TRUE) ~ "DDE",
    grepl("o,p-Dichlorodiphenyldichloroethylene \\(o,p-DDE\\)", ocp, ignore.case = TRUE) ~ "DDE",
    TRUE ~ ocp
  ))
}
 
# Transform the data 
alluvial <- im %>%
  left_join(ocp, by = "study_id") %>%
  left_join(sub, by = "study_id") %>%
  separate_rows(subject, sep = ",\\s+") %>% 
  separate_rows(ocp, sep = ",\\s+") %>% 
  separate_rows(impact_broad, sep = ",\\s+") %>%
  replace_ocp2() %>%
  filter(!grepl("not reported", ocp, ignore.case = TRUE)) %>%
  group_by(ocp, subject, impact_broad) %>%
  summarise(freq = n(), .groups = 'drop') %>%
  group_by(ocp) %>%
  filter(sum(freq) > 10) %>%  
  group_by(impact_broad) %>% 
  filter(sum(freq) > 5) %>% 
  mutate(subject = factor(subject, levels = c("Environment", "Non-human animal", "Human"), ordered = TRUE))

# Make alluvial plot
figs29 <- alluvial %>% 
  ggplot(
       aes(axis1 = ocp, axis2 = subject, axis3 = impact_broad, y = freq)) +
  scale_x_discrete(limits = c("Organochlorine Pesticide", "Subject", "Impact"), expand = c(.05, .10)) +
  xlab("Variables") +
  ylab("Frequency") +  
  geom_alluvium(aes(fill = subject)) +
  geom_stratum(width = 1/2, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 5, fontface = "bold") +  
  theme_minimal() +
  theme(axis.text=element_text(size=15),  
        axis.title=element_text(size=20),
        legend.position="none",
        panel.grid.major = element_blank(),  
        panel.grid.minor = element_blank()) + 
  scale_fill_brewer(palette = "Dark2", name = "Subject Category")

figs29

# ggsave(here("figures", "figs29.pdf"), width =18, height = 12, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs29.jpg"), width = 18, height = 12, units = "cm", scale = 2, dpi = 800)

# Join the organochlorine pesticide details with the impact details
ocp_im <- left_join(ocp, im ,sub, by = "study_id") 

# Separate rows in "ocp_im"
ocp_im1 <- separate_rows(ocp_im, ocp , sep = ", ", convert = TRUE)


# Group by "ocp" and "impact" and summarize count
ocp_im_summary <- ocp_im1 %>%
  mutate(ocp = str_trim(ocp),
         impact_broad = str_trim(impact_broad)) %>%
    replace_ocp2() %>% 
  group_by(ocp, impact_broad) %>%
  summarise(count = n(), .groups = "drop") %>% 
  mutate(ocp = ifelse(grepl("no chemical classification", ocp, ignore.case = TRUE), "no chemical\n classification", ocp))

# Filter for top 8 pesticides 
top_pesticides <- ocp_im_summary %>%
  filter(ocp != "not reported") %>%
  group_by(ocp) %>%
  summarise(total_count = sum(count)) %>%
  top_n(8, total_count) %>%
  pull(ocp) 


ocp_im_summary_filtered <- ocp_im_summary %>%
  filter(ocp %in% top_pesticides)

fig4 <- ocp_im_summary_filtered %>% 
  ggplot(aes(x = fct_rev(fct_reorder(impact_broad, count, .fun = 'sum')),
             y = fct_reorder(ocp, count, .fun = 'sum'),
             size = count,
             fill = impact_broad)) +
  geom_point(shape = 21, color = "white") +
  geom_text(aes(label = count), size = 20, fontface = "bold") +
  labs(title = NULL, x = NULL, y =NULL) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(size = 35),  
    axis.text.y = element_text(size = 40, hjust = 1),  
    legend.position = "none") +
  scale_size_continuous(range = c(30, 80))

fig4

 ggsave(here("figures", "fig4.pdf"), width = 40, height = 25, units = "cm", scale = 2, dpi = 800)
 ggsave(here("figures", "fig4.jpg"), width = 40, height = 25, units = "cm", scale = 2, dpi = 800)

bibliometric_analysis <- biblioAnalysis(bib_sco)
par(cex=1.5)
plot(bibliometric_analysisbibliometric_analysis)

## Error in plot(bibliometric_analysisbibliometric_analysis): object 'bibliometric_analysisbibliometric_analysis' not found

summary(bibliometric_analysis)

## 
## 
## MAIN INFORMATION ABOUT DATA
## 
##  Timespan                              1993 : 2022 
##  Sources (Journals, Books, etc)        45 
##  Documents                             100 
##  Annual Growth Rate %                  9.25 
##  Document Average Age                  8.78 
##  Average citations per doc             58.6 
##  Average citations per year per doc    5.676 
##  References                            7548 
##  
## DOCUMENT TYPES                     
##  article               50 
##  conference paper      2 
##  review                48 
##  
## DOCUMENT CONTENTS
##  Keywords Plus (ID)                    1592 
##  Author's Keywords (DE)                258 
##  
## AUTHORS
##  Authors                               544 
##  Author Appearances                    684 
##  Authors of single-authored docs       1 
##  
## AUTHORS COLLABORATION
##  Single-authored docs                  1 
##  Documents per Author                  0.184 
##  Co-Authors per Doc                    6.84 
##  International co-authorships %        41 
##  
## 
## Annual Scientific Production
## 
##  Year    Articles
##     1993        1
##     1995        1
##     1999        1
##     2000        1
##     2004        2
##     2006        3
##     2007        1
##     2008        2
##     2009        1
##     2010        4
##     2011        3
##     2012        5
##     2013        5
##     2014        9
##     2015        7
##     2016       11
##     2017        4
##     2018        2
##     2019        7
##     2020        8
##     2021        9
##     2022       13
## 
## Annual Percentage Growth Rate 9.25 
## 
## 
## Most Productive Authors
## 
##       Authors        Articles    Authors        Articles Fractionalized
## 1  LISON D                  7 LISON D                             2.000
## 2  VAN MAELE-FABRY G        7 VAN MAELE-FABRY G                   2.000
## 3  BONDE JP                 5 DAVIS WJ                            1.000
## 4  BALLESTER F              4 GAMET-PAYRASTRE L                   0.867
## 5  CHEVRIER C               4 KREWSKI D                           0.867
## 6  EGGESBØ M                4 HOET P                              0.833
## 7  GOVARTS E                4 FU X                                0.750
## 8  SCHOETERS G              4 MUÑOZ CC                            0.750
## 9  TRNOVEC T                4 VERMEIREN P                         0.750
## 10 ZHANG Y                  4 LEVY LS                             0.700
## 
## 
## Top manuscripts per citations
## 
##                                       Paper                                      DOI  TC TCperYear  NTC
## 1  BROWN TP, 2006, ENVIRON HEALTH PERSPECT           10.1289/ehp.8095                329     17.32 1.85
## 2  GOVARTS E, 2012, ENVIRON HEALTH PERSPECT          10.1289/ehp.1103767             228     17.54 1.81
## 3  PEZZOLI G, 2013, NEUROLOGY                        10.1212/WNL.0b013e318294b3c8    207     17.25 2.08
## 4  BONDE JP, 2016, HUM REPROD UPDATE                 10.1093/HUMUPD/DMW036           188     20.89 2.24
## 5  RIGÉT F, 2010, SCI TOTAL ENVIRON                  10.1016/j.scitotenv.2009.07.036 162     10.80 1.50
## 6  VAN DER MARK M, 2012, ENVIRON HEALTH PERSPECT     10.1289/ehp.1103881             161     12.38 1.28
## 7  OJAJÄRVI IA, 2000, OCCUP ENVIRON MED              10.1136/oem.57.5.316            153      6.12 1.00
## 8  SCHINASI L, 2014, INT J ENVIRON RES PUBLIC HEALTH 10.3390/ijerph110404449         148     13.45 2.90
## 9  SONG Y, 2016, J DIABETES                          10.1111/1753-0407.12325         140     15.56 1.67
## 10 ADAMI HO, 1995, CANCER CAUSES CONTROL             10.1007/BF00054165              140      4.67 1.00
## 
## 
## Corresponding Author's Countries
## 
##           Country Articles   Freq SCP MCP MCP_Ratio
## 1  CHINA                17 0.1753  12   5     0.294
## 2  USA                  11 0.1134   9   2     0.182
## 3  BELGIUM               7 0.0722   5   2     0.286
## 4  CANADA                6 0.0619   3   3     0.500
## 5  FRANCE                6 0.0619   2   4     0.667
## 6  BRAZIL                5 0.0515   5   0     0.000
## 7  DENMARK               5 0.0515   0   5     1.000
## 8  SPAIN                 5 0.0515   0   5     1.000
## 9  NETHERLANDS           4 0.0412   3   1     0.250
## 10 UNITED KINGDOM        4 0.0412   3   1     0.250
## 
## 
## SCP: Single Country Publications
## 
## MCP: Multiple Country Publications
## 
## 
## Total Citations per Country
## 
##      Country      Total Citations Average Article Citations
## 1  DENMARK                    687                     137.4
## 2  USA                        686                      62.4
## 3  CHINA                      607                      35.7
## 4  UNITED KINGDOM             567                     141.8
## 5  BELGIUM                    488                      69.7
## 6  CANADA                     417                      69.5
## 7  FRANCE                     389                      64.8
## 8  NETHERLANDS                259                      64.8
## 9  ITALY                      207                     207.0
## 10 SPAIN                      181                      36.2
## 
## 
## Most Relevant Sources
## 
##                                  Sources        Articles
## 1  ENVIRONMENTAL HEALTH PERSPECTIVES                  10
## 2  SCIENCE OF THE TOTAL ENVIRONMENT                    9
## 3  ENVIRONMENT INTERNATIONAL                           7
## 4  CANCER CAUSES AND CONTROL                           5
## 5  ENVIRONMENTAL RESEARCH                              5
## 6  ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH        5
## 7  ENVIRONMENTAL SCIENCE AND TECHNOLOGY                4
## 8  SCIENTIFIC REPORTS                                  4
## 9  CHEMOSPHERE                                         3
## 10 ENVIRONMENTAL POLLUTION                             3
## 
## 
## Most Relevant Keywords
## 
##    Author Keywords (DE)      Articles Keywords-Plus (ID)     Articles
## 1      META-ANALYSIS               43 ENVIRONMENTAL EXPOSURE       92
## 2      PESTICIDES                  28 HUMAN                        84
## 3      SYSTEMATIC REVIEW           18 PESTICIDE                    79
## 4      OCCUPATIONAL EXPOSURE        8 HUMANS                       71
## 5      DDT                          7 FEMALE                       70
## 6      CHILD                        6 META ANALYSIS                64
## 7      DDE                          5 PESTICIDES                   60
## 8      BREAST CANCER                4 OCCUPATIONAL EXPOSURE        57
## 9      INSECTICIDES                 4 MALE                         56
## 10     ORGANOCHLORINES              4 PRIORITY JOURNAL             53

str(bibliometric_analysis)

## List of 26
##  $ Articles            : int 100
##  $ Authors             : 'table' int [1:544(1d)] 7 7 5 4 4 4 4 4 4 4 ...
##   ..- attr(*, "dimnames")=List of 1
##   .. ..$ AU: chr [1:544] "LISON D" "VAN MAELE-FABRY G" "BONDE JP" "BALLESTER F" ...
##  $ AuthorsFrac         :'data.frame':    544 obs. of  2 variables:
##   ..$ Author   : chr [1:544] "LISON D" "VAN MAELE-FABRY G" "DAVIS WJ" "GAMET-PAYRASTRE L" ...
##   ..$ Frequency: num [1:544] 2 2 1 0.867 0.867 ...
##  $ FirstAuthors        : chr [1:100] "LAMAT H" "YIPEI Y" "MOTA TFM" "HE H" ...
##  $ nAUperPaper         : int [1:100] 8 7 4 6 5 6 2 5 5 2 ...
##  $ Appearances         : int 684
##  $ nAuthors            : int 544
##  $ AuMultiAuthoredArt  : int 543
##  $ AuSingleAuthoredArt : int 1
##  $ MostCitedPapers     :'data.frame':    100 obs. of  5 variables:
##   ..$ Paper         : chr [1:100] "BROWN TP, 2006, ENVIRON HEALTH PERSPECT" "GOVARTS E, 2012, ENVIRON HEALTH PERSPECT" "PEZZOLI G, 2013, NEUROLOGY" "BONDE JP, 2016, HUM REPROD UPDATE" ...
##   ..$ DOI           : chr [1:100] "10.1289/ehp.8095" "10.1289/ehp.1103767" "10.1212/WNL.0b013e318294b3c8" "10.1093/HUMUPD/DMW036" ...
##   ..$ TC            : num [1:100] 329 228 207 188 162 161 153 148 140 140 ...
##   ..$ TCperYear     : num [1:100] 17.3 17.5 17.2 20.9 10.8 ...
##   ..$ NTC           : num [1:100] 1.85 1.81 2.08 2.24 1.5 ...
##  $ Years               : num [1:100] 2022 2022 2022 2022 2022 ...
##  $ FirstAffiliation    : chr [1:100] "UNIVERSITÉ CLERMONT AUVERGNE" "PEKING UNIVERSITY HEALTH SCIENCE CENTER" "UNIVERSIDADE ESTADUAL DO PARANÁ (UNESPAR)" "ANHUI MEDICAL UNIVERSITY" ...
##  $ Affiliations        : 'table' int [1:289(1d)] 7 7 6 6 6 5 5 5 5 5 ...
##   ..- attr(*, "dimnames")=List of 1
##   .. ..$ AFF: chr [1:289] "UNIVERSITÉ CATHOLIQUE DE LOUVAIN" "UNIVERSITY OF COPENHAGEN" "HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY" "IMPERIAL COLLEGE LONDON" ...
##  $ Aff_frac            :'data.frame':    289 obs. of  2 variables:
##   ..$ Affiliation: chr [1:289] "UNIVERSITÉ CATHOLIQUE DE LOUVAIN" "HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY" "UNIVERSIDADE FEDERAL DO PARANÁ" "UNIVERSITY OF OTTAWA" ...
##   ..$ Frequency  : num [1:289] 4.67 2.67 2 1.89 1.5 ...
##  $ CO                  : chr [1:100] "FRANCE" "CHINA" "BRAZIL" "CHINA" ...
##  $ Countries           : 'table' int [1:28(1d)] 17 11 7 6 6 5 5 5 4 4 ...
##   ..- attr(*, "dimnames")=List of 1
##   .. ..$ Tab: chr [1:28] "CHINA" "USA" "BELGIUM" "CANADA" ...
##  $ CountryCollaboration:'data.frame':    28 obs. of  3 variables:
##   ..$ Country: chr [1:28] "CHINA" "USA" "BELGIUM" "CANADA" ...
##   ..$ SCP    : num [1:28] 12 9 5 3 2 5 0 0 3 3 ...
##   ..$ MCP    : num [1:28] 5 2 2 3 4 0 5 5 1 1 ...
##  $ TotalCitation       : num [1:100] 4 1 3 4 8 2 22 11 1 1 ...
##  $ TCperYear           : num [1:100] 1.333 0.333 1 1.333 2.667 ...
##  $ Sources             : 'table' int [1:45(1d)] 10 9 7 5 5 5 4 4 3 3 ...
##   ..- attr(*, "dimnames")=List of 1
##   .. ..$ SO: chr [1:45] "ENVIRONMENTAL HEALTH PERSPECTIVES" "SCIENCE OF THE TOTAL ENVIRONMENT" "ENVIRONMENT INTERNATIONAL" "CANCER CAUSES AND CONTROL" ...
##  $ DE                  : 'table' int [1:258(1d)] 43 28 18 8 7 6 5 4 4 4 ...
##   ..- attr(*, "dimnames")=List of 1
##   .. ..$ Tab: chr [1:258] "META-ANALYSIS" "PESTICIDES" "SYSTEMATIC REVIEW" "OCCUPATIONAL EXPOSURE" ...
##  $ ID                  : 'table' int [1:1592(1d)] 92 84 79 71 70 64 60 57 56 53 ...
##   ..- attr(*, "dimnames")=List of 1
##   .. ..$ Tab: chr [1:1592] "ENVIRONMENTAL EXPOSURE" "HUMAN" "PESTICIDE" "HUMANS" ...
##  $ Documents           : 'table' int [1:3(1d)] 50 2 48
##   ..- attr(*, "dimnames")=List of 1
##   .. ..$ : chr [1:3] "ARTICLE              " "CONFERENCE PAPER     " "REVIEW               "
##  $ IntColl             : num 41
##  $ nReferences         : int 7548
##  $ DB                  : chr "SCOPUS"
##  - attr(*, "class")= chr "bibliometrix"

figs30_data <- bibliometric_analysis$CountryCollaboration
figs30_tibble <- as_tibble(figs33_data)

## Error in as_tibble(figs33_data): object 'figs33_data' not found

figs30_tibble_long <- figs30_tibble %>% 
                      pivot_longer(cols = c(SCP, MCP), names_to = "Country_Collaboration", values_to = "value")

## Error in pivot_longer(., cols = c(SCP, MCP), names_to = "Country_Collaboration", : object 'figs30_tibble' not found

# Summarizing and filtering for top ten countries
top_countries <- figs30_tibble_long %>%
  group_by(Country) %>%
  summarize(Total = sum(value)) %>%
  arrange(desc(Total)) %>%
  top_n(10, Total)

## Error in group_by(., Country): object 'figs30_tibble_long' not found

# Filtering the original dataset to keep only the top ten countries
figs30_tibble_long_filtered <- figs30_tibble_long %>%
  filter(Country %in% top_countries$Country)

## Error in filter(., Country %in% top_countries$Country): object 'figs30_tibble_long' not found

figs30 <- ggplot(figs30_tibble_long_filtered, aes(fill = Country_Collaboration, y = value, x = Country)) +
  geom_bar(position = "stack", stat = "identity") +
  coord_flip() +
  organochlorTHEME()

## Error in ggplot(figs30_tibble_long_filtered, aes(fill = Country_Collaboration, : object 'figs30_tibble_long_filtered' not found

figs30

## Error in eval(expr, envir, enclos): object 'figs30' not found

# ggsave(here("figures", "figs30.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs30.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800) 

figs31_data <- bibliometric_analysis$Authors
figs31_tibble <- as_tibble(figs34_data)

## Error in as_tibble(figs34_data): object 'figs34_data' not found

figs31_tibble_filtered <- figs34_tibble %>% 
                          slice_head(n=10) 

## Error in slice_head(., n = 10): object 'figs34_tibble' not found

figs31 <- ggplot(figs31_tibble_filtered, aes(y = n, x = reorder(AU,n))) +
          geom_bar(stat = "identity", width = 0.8 , alpha = 0.7, fill = "#1b9e77") +
          geom_text(aes(label = n, y = n/2), hjust = 0.5, color = "black", size = 10) +
          coord_flip() +
          organochlorTHEME()

## Error in ggplot(figs31_tibble_filtered, aes(y = n, x = reorder(AU, n))): object 'figs31_tibble_filtered' not found

# ggsave(here("figures", "figs31.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs31.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800) 

# Extract country information from the "AU1_CO" and "AU_CO" fields of the "bib_sco" dataset
bibmap <- metaTagExtraction(bib_sco, Field = "AU1_CO", sep = ";") 
bibmap <- metaTagExtraction(bibmap, Field = "AU_CO", sep = ";") 

# Create a data frame with counts of articles from each country
firstcountrycounts <- bibmap %>% 
  group_by(AU1_CO) %>% 
  count() %>% 
  filter(!is.na(AU1_CO))  

# Load world map data and remove countries with longitude >180 to make an equal projection-like map
world_map <- map_data("world") %>% 
  filter(! long > 180)

# Format country names to match regions on the world map
firstcountrycounts$region <- str_to_title(firstcountrycounts$AU1_CO)
firstcountrycounts$region[firstcountrycounts$region == "Usa"] <- "USA" 
firstcountrycounts$region[firstcountrycounts$region == "Korea"] <- "South Korea"
firstcountrycounts$region[firstcountrycounts$region == "United Kingdom"] <- "UK"

# Join count data with map data and set missing counts to zero
emptymap <- tibble(region = unique(world_map$region), n = rep(0,length(unique(world_map$region))))
fullmap <- left_join(emptymap, firstcountrycounts, by = "region")
fullmap$n <- fullmap$n.x + fullmap$n.y
fullmap$n[is.na(fullmap$n)] <- 0

fig5 <- fullmap %>%
  ggplot(aes(fill = n, map_id = region)) +
  geom_map(map = world_map, color = "gray50") +
  expand_limits(x = world_map$long, y = world_map$lat) +
  coord_map("mercator", xlim = c(-180, 180), ylim = c(-50, 90)) +  
  theme(
    axis.text = element_blank(),  
    axis.title = element_blank(),  
    legend.position = "bottom",
    legend.box = "horizontal",  
    legend.box.just = "center",  
    legend.margin = margin(t = 10, unit = "pt"),  
    legend.text = element_text(size = 20),  
    legend.title = element_text(size = 20, face = "bold"),  
    legend.key.width = unit(30, "mm"), 
    panel.background = element_rect(fill = "white")
  ) +
  scale_fill_gradient(
    low = "#98FB98", high = "#006400",
    name = "First Author Count",  
    na.value = "gray70",
    limits = c(1, 20)
  ) +
  guides(
    fill = guide_colourbar(
      barwidth = unit(210, units = "mm"),
      barheight = unit(5, units = "mm")
    )
  )

fig5

 # ggsave(here("figures", "fig5.pdf"), width = 21, height = 12, units = "cm", scale = 2, dpi = 800)
 # ggsave(here("figures", "fig5.jpg"), width = 21, height = 12, units = "cm", scale = 2, dpi = 800)

figs38 <- fullmap %>%
ggplot(aes(fill = n, map_id = region)) +
geom_map(map = world_map, color = "gray50") +
coord_map("mercator", ylim = c(35, 65), xlim = c(-35, 45)) + 
theme(
    axis.text = element_blank(),  
    axis.title = element_blank(),  
    legend.box = "horizontal",  
    legend.box.just = "center",  
    legend.margin = margin(t = 10, unit = "pt"),  
    legend.text = element_text(size = 16),  
    legend.title = element_text(size = 18, face = "bold"),  
    legend.key.width = unit(30, "mm"),
    legend.position = "bottom",
    panel.background = element_rect(fill = "white")
) +
scale_fill_gradient(low = "#98FB98", high = "#006400",
    name = "First Author Count", na.value = "gray70",
    limits = c(1, 10), breaks = seq(0, 10, length.out = 6)) 

figs38

# ggsave(here("figures", "figs38.pdf"), width = 16, height = 12, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs38.jpg"), width = 16, height = 12, units = "cm", scale = 2, dpi = 800)

jpeg(filename = here("figures", "figs39.jpg"), width = 800, height = 800)

# Extract countries from the affiliations
bib_sco2 <- metaTagExtraction(bib_sco, Field = "AU_CO", sep = ";")

# Create a network matrix of collaborations between countries
NetMatrix_country <- biblioNetwork(bib_sco2, analysis = "collaboration", network = "countries", sep = ";")

# Convert the network matrix to a standard matrix
NetMatrix_country <- as.matrix(NetMatrix_country)

# Remove the lower triangle (as this is duplication of info)
NetMatrix_country[lower.tri(NetMatrix_country)] <- 0 

# Change column and row names to title case
colnames(NetMatrix_country) <- str_to_title(colnames(NetMatrix_country))
rownames(NetMatrix_country) <- str_to_title(rownames(NetMatrix_country))

# Change "Usa" to "USA"
colnames(NetMatrix_country)[colnames(NetMatrix_country) == "Usa"] <- "USA"
rownames(NetMatrix_country)[rownames(NetMatrix_country) == "Usa"] <- "USA"

# Change "United Kingdom" to "UK" for easier plotting
colnames(NetMatrix_country)[colnames(NetMatrix_country) == "United Kingdom"] <- "UK"
rownames(NetMatrix_country)[rownames(NetMatrix_country) == "United Kingdom"] <- "UK"

# Setting up custom parameters
circos.par(cell.padding = c(0, 0, 0, 0), track.margin = c(0, 0))

# Create a chord diagram of the network matrix
 chordDiagram(NetMatrix_country, annotationTrack = "grid", preAllocateTracks = 1)

# Add a track to label each sector with its name
circos.trackPlotRegion(track.index = 1, bg.border = NA, panel.fun = function(x, y) {
  xlim = get.cell.meta.data("xlim")
  ylim = get.cell.meta.data("ylim")
  sector.name = get.cell.meta.data("sector.index")
  circos.text(mean(xlim), ylim[1] + 0.2 , sector.name, facing = "clockwise", niceFacing = TRUE, adj = c(0, 0))
  circos.axis(h = "top", labels.cex = 0.5, major.tick.length = 0.2, sector.index = sector.name, track.index = 2)
})

dev.off()

## png 
##   2

jpeg(filename = here("figures", "figs40.jpg"), width = 800, height = 800)

diag(NetMatrix_country) <- 0

# Create a chord diagram of the network matrix
figs43 <- chordDiagram(NetMatrix_country, annotationTrack = "grid", preAllocateTracks = 1)

# Add a track to label each sector with its name
circos.trackPlotRegion(track.index = 1, panel.fun = function(x, y) {
  xlim = get.cell.meta.data("xlim")
  ylim = get.cell.meta.data("ylim")
  sector.name = get.cell.meta.data("sector.index")
  circos.text(mean(xlim), ylim[1] + 0.2, sector.name, facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.5))
  circos.axis(h = "top", labels.cex = 0.5, major.tick.length = 0.2, sector.index = sector.name, track.index = 2)
}, bg.border = NA)

dev.off()

## png 
##   2

# Country rename to continents
NetMatrix_continent <- NetMatrix_country
colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "USA"] <- "North America"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "USA"] <- "North America"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Spain"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Spain"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "China"] <- "Asia"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "China"] <- "Asia"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "France"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "France"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Canada"] <- "North America"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Canada"] <- "North America"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Denmark"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Denmark"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Netherlands"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Netherlands"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Belgium"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Belgium"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "UK"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "UK"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Australia"] <- "Other"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Australia"] <- "Other"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Brazil"] <- "Other"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Brazil"] <- "Other"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Germany"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Germany"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Finland"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Finland"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Greece"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Greece"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Korea"] <- "Asia"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Korea"] <- "Asia"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Norway"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Norway"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Sweden"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Sweden"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Egypt"] <- "Other"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Egypt"] <- "Other"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Italy"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Italy"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Japan"] <- "Asia"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Japan"] <- "Asia"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Portugal"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Portugal"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Switzerland"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Switzerland"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Turkey"] <- "Asia"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Turkey"] <- "Asia"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "India"] <- "Asia"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "India"] <- "Asia"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Iran"] <- "Asia"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Iran"] <- "Asia"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Costa Rica"] <- "North America"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Costa Rica"] <- "North America"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Czech Republic"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Czech Republic"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Hong Kong"] <- "Asia"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Hong Kong"] <- "Asia"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Iceland"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Iceland"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Ireland"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Ireland"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Mexico"] <- "North America"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Mexico"] <- "North America"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Romania"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Romania"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Slovakia"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Slovakia"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Ukraine"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Ukraine"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Australia"] <- "Other"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Australia"] <- "Other"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Kazakhstan"] <- "Asia"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Kazakhstan"] <- "Asia"


# collapsing
merge_matrix <- t(rowsum(t(NetMatrix_continent), group = colnames(NetMatrix_continent), na.rm = T))
merge_matrix2 <- rowsum(merge_matrix, group = rownames(merge_matrix))


# remove diagonal elements
diag(merge_matrix2) <- 0

# chord plot
chordDiagramFromMatrix(merge_matrix2)


# Define colors
my_colors <- c("#1B9E77", "#D95F02", "#7570B3", "#E6AB02")

my_colors <- rep(my_colors, length.out = ncol(merge_matrix2))


jpeg("figures//figs42.jpeg", width = 16, height = 16, units = "cm", res = 300)

## Error in jpeg("figures//figs42.jpeg", width = 16, height = 16, units = "cm", : unable to start jpeg() device

# Create a chord diagram of the network matrix
fig6 <- chordDiagram(small_matrix, 
                       annotationTrack = "grid", 
                       annotationTrackHeight = mm_h(7), 
                       preAllocateTracks = 1,
                       grid.col = my_colors2)

## Error in chordDiagram(small_matrix, annotationTrack = "grid", annotationTrackHeight = mm_h(7), : object 'small_matrix' not found

# Add a track to label each sector with its name
circos.trackPlotRegion(track.index = 1, panel.fun = function(x, y) {
  xlim = get.cell.meta.data("xlim")
  ylim = get.cell.meta.data("ylim")
  sector.name = get.cell.meta.data("sector.index")
  circos.text(mean(xlim), ylim[1], sector.name, facing = "bending.inside", niceFacing = TRUE, adj = c(0.5, 1.75), col = "white", cex = 1.2)
}, bg.border = NA)

dev.off()

## null device 
##           1

fields <- sd %>%
  mutate(
    title = str_to_lower(paper_title),
    ntitle = paste(str_to_lower(str_split_fixed(study_id, "\\_", n = 2)[,1]), paper_title, sep = " "),
    ntitle = str_squish(ntitle),
    Journal_Category_Allocated_Broad = str_replace_all(Journal_Category_Allocated_Broad, "Toxicology", "Other"),
    Journal_Category_Allocated_Broad = str_replace_all(Journal_Category_Allocated_Broad, "Multidisciplinary", "Multi\ndisciplinary"),
    Journal_Category_Allocated_Broad = str_replace_all(Journal_Category_Allocated_Broad, "Marine Biology", "Other"),
    Journal_Category_Allocated_Broad = str_replace_all(Journal_Category_Allocated_Broad, "Environmental Science", "Environemental\nScience"),    Journal_Category_Allocated_Broad = str_replace_all(Journal_Category_Allocated_Broad, "Psychology", "Other")
  ) %>% 
  select(ntitle, Journal_Category_Allocated_Broad)

bib_names <- bib_sco %>% rownames_to_column(., var = "mat_names") %>% 
  mutate(TI2 = tolower(unlist(lapply(data.frame(t(str_split_fixed(TI, " ", n = 15)[,1:14])), 
                                     function(x) str_c(x, collapse  = " ")))),
         name2 = stri_trans_general(tolower(str_split_fixed(SR, " ", n = 2)[,1]), "latin-ascii"),
         TI2 = paste(name2, TI2, sep= " "),
         TI2 = trimws(TI2)) %>% 
  select(TI2, mat_names)

pos <- lapply(bib_names$TI2, function(x) stringdist(fields$ntitle, x))
pos2<- map_dbl(pos, which.min)

# now we can merge two datasets 
bib_names$Journal_Category_Allocated_Broad <- fields$Journal_Category_Allocated_Broad[pos2]

# Creating matrix for bibliometric coupling
NetMatrix <- biblioNetwork(bib_sco, analysis = "coupling", network = "references", sep = ";")

# forcing into a nromal matrix
net_matrix <- as.matrix(NetMatrix)

diag(net_matrix) <- 0 #get rid of counts for the same papers

# replacing names with Journal_Category_Allocated_Broad
rownames(net_matrix) <- bib_names$Journal_Category_Allocated_Broad
colnames(net_matrix) <- bib_names$Journal_Category_Allocated_Broad

# reducing matrix according to discipline_code
rect_matrix<- t(rowsum(t(net_matrix), group = colnames(net_matrix), na.rm = T))
small_matrix <- rowsum(rect_matrix, group = rownames(rect_matrix))

# getting rid of lower triangle (as this is duplication of info)
small_matrix[lower.tri(small_matrix)] <- 0 
par(mar = c(0, 0, 0, 0), mfrow = c(1, 1))

# diag(small_matrix) <- 0

# Define colors
my_colors2 <- c("#1B9E77", "#D95F02", "#7570B3",  "#E6AB02")

my_colors2 <- rep(my_colors2, length.out = ncol(small_matrix))

jpeg("figures//figs42.jpeg", width = 20, height = 20, units = "cm", res = 300)

## Error in jpeg("figures//figs42.jpeg", width = 20, height = 20, units = "cm", : unable to start jpeg() device

# Create a chord diagram with specified colors
figs47 <- chordDiagram(small_matrix, 
                       annotationTrack = "grid", 
                       annotationTrackHeight = mm_h(7), 
                       preAllocateTracks = 1,
                       grid.col = my_colors2)

# Add a track to label each sector with its name
circos.trackPlotRegion(track.index = 1, panel.fun = function(x, y) {
  xlim = get.cell.meta.data("xlim")
  ylim = get.cell.meta.data("ylim")
  sector.name = get.cell.meta.data("sector.index")
  circos.text(mean(xlim), ylim[1] , sector.name, facing = "clockwise", niceFacing = TRUE, adj = c(-0.1, 0.5), col = "black", cex = 1)
}, bg.border = NA)

dev.off()

## null device 
##           1

---
title: "Sixty years since Silent Spring: a map of meta-analyses on organochlorine pesticides reveals urgent needs for improving methodological quality"
author: "Kyle Morrison, Coralie Williams, Lorenzo Ricolfi, Yefeng Yang, Malgorzata Lagisz, Shinichi Nakagawa" 
date: "2023-03-09"
output: 
  rmdformats::downcute:
    code_folding: show
    code_download: true
    toc_depth: 4
editor_options: 
  chunk_output_type: console
  
---
```{r, include = FALSE}
rm(list = ls())
# knitr setting
knitr::opts_chunk$set(
  message = FALSE,
  warning = FALSE, 
  cache = TRUE,
  error = TRUE, 
  echo=TRUE
)
```


In this Rmarkdown document we provide the following workflow:   

- Section 0: To examine the volume and temporal trends of existing meta-analyses on the effects of organochlorine pesticides

- Section 1: To evaluate the methodological patterns and quality of existing meta-analyses studying the effects of organochlorine pesticides.

- Section 2: To explore the various characteristics of the organochlorine pesticides literature such as the pesticides used, the impacts elicited in response and the subjects that were investigated.

- Section 3: To investigate the research outputs across different countries, continents and disciplines, and investigate the degree of cross-country collaboration.
 

# Load packages and data
## Load packages 
```{r, results="hide"}
rm(list = ls())
pacman::p_load(tidyverse,
hrbrthemes, 
patchwork,
here,
stringr,
knitr,
formatR,
forcats,
ggplot2,
bibliometrix,
igraph,
stringi,
stringdist,
circlize,
ggalluvial,
ggraph)
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)

```


## Load data
Manually extracted pilot data is stored in five separate **.csv** files representing different aspects of the data (extracted via structured predefined Google Forms - one per table). 
Bibliographic data records are exported from Scopus (including cited references field) in .bib format and locally saved as **bib_sco.bib**. 
```{r,  results="hide"}
# Load CSV datasets
sd <- read_csv(here("data", "ocp_srm_study_details.csv"))
ocp <- read_csv(here("data", "ocp_srm_ocp_details.csv"))
sub <- read_csv(here("data", "ocp_srm_subject_details.csv"))
im <- read_csv(here("data", "ocp_srm_impact_details.csv"))
sp <- read_csv(here("data", "ocp_srm_species_details.csv"))

# Load BibTeX dataset
bib_sco <- convert2df(here("data", "bib_sco.bib"), dbsource = "scopus", format = "bibtex")
```

# Section 0 
To examine the volume and temporal trends of existing meta-analyses on the effects of organochlorine pesticides

## Figure 1 
A) Bar chart showing the annual number of meta-analyses synthesising research on the impacts of organochlorine pesticides, categorised by different subjects of exposure. 
B) Area graph showing the cumulative time trends of meta-analyses synthesising research on the impacts of organochlorine pesticides, categorised by different subjects of exposure.

```{r, fig.width=16, fig.height=12}
# Join the study and subject datasets
sd_sub <- left_join(sd, sub, by = "study_id")

# Count the number of publications per year and calculate cumulative sum
sd1 <- sd %>%
  count(publication_year) %>%
  mutate(n_cumulative = cumsum(n))


# Create a theme for the count plots 
theme_count <- function() {
  theme_minimal() +
  theme(
    panel.grid.major.y = element_line(color = "gray", linetype = "dashed"),
    panel.grid.minor.y = element_blank(),
    axis.line.x = element_line(size = 1.2),
    axis.line.y = element_line(size = 1.2),
    axis.title.x = element_text(size = 20, face = "bold"),
    axis.title.y = element_text(size = 20, face = "bold"),
    axis.text.x = element_text(angle = 45, hjust = 1, size = 15, color = "#666666"),
    axis.text.y = element_text(size = 20, color = "#666666"),
    legend.position = "none"
  )
}


# Create the annual count plot
fig1a <- sd_sub %>%
  mutate(subjects = strsplit(subject, ",\\s+")) %>%
  unnest(subjects) %>%
  count(publication_year, subjects) %>%
  group_by(subjects = reorder(subjects, n)) %>%
  ggplot(aes(x = publication_year, y = n, fill = subjects)) +
    geom_bar(stat = "identity", position = "stack", alpha = 0.7) +
    geom_text(aes(label = n), position = position_stack(vjust = 0.6), 
              fontface = "bold", color = "white", size = 6, hjust = 0.4) +
    scale_x_continuous(breaks = seq(min(sd1$publication_year), max(sd1$publication_year), by = 1), 
                       expand = expansion(0,0)) +  
    scale_y_continuous("Annual Article Count", limits = c(0,15)) +
    labs(y = "Article Count", tag = "A", fill = "Subjects") +
    theme_count() +
    theme(axis.title.x = element_blank()) +
    scale_fill_brewer(palette = "Dark2") +
    guides(fill = guide_legend(override.aes = list(size=10)))


# Create the cumulative count plot
fig1b <- sd_sub %>%
  mutate(subjects = strsplit(subject, ",\\s+")) %>%
  unnest(subjects) %>%
  count(publication_year, subjects) %>%
  group_by(subjects = reorder(subjects, n)) %>%
  mutate(n_cumulative = cumsum(n)) %>%
  ggplot(aes(x = publication_year, y = n_cumulative, fill = subjects)) +
    geom_area(size = 1.2, alpha = 0.7) +
    scale_y_continuous("Cumulative Article Count", limits = c(0,115)) +
    scale_x_continuous(breaks = seq(min(sd_sub$publication_year), max(sd_sub$publication_year), by = 1), 
                       expand = expansion(0,0)) +  
    labs(x = "Year", y = "Cumulative Article Count", tag = "B", fill = "Subjects") +
    theme_count() +
    scale_fill_brewer(palette = "Dark2") +
    guides(fill = guide_legend(override.aes = list(size=10)))

# Combine the two plots
fig1 <- fig1a / fig1b
fig1

# ggsave(here("figures", "fig1.pdf"), width = 16, height = 12, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "fig1.jpg"), width = 16, height = 12, units = "cm", scale = 2, dpi = 800)


# Total number of included primary literature
 total_sum_primary <- sd %>%
  summarise(total_sum = sum(total_included_studies, na.rm = TRUE)) %>%
  pull(total_sum)

 total_sum_primary # 3911
```

## 
Create a standard theme for the supplementary plots
```{r}
organochlorTHEME <- function() {
  theme_minimal() +
  theme(
    title = element_text(size = 30),
    axis.text.y = element_text(size = 30),
    axis.text.x = element_text(size = 20),
    axis.line.x = element_line(color = "gray", size = 3),
    axis.line.y = element_blank(),
    axis.ticks.x = element_blank(),
    axis.ticks.y = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.y = element_blank(),
    legend.position = "none")    
}

```


## Figure s4 
A) Bar chart showing the annual number of published meta-analyses synthesising research on the impacts of organochlorine pesticides.  
B) Line graph showing the cumulative time trends of meta-analyses synthesising research on the impacts of organochlorine pesticides.
```{r, fig.width=16, fig.height=12}

# Create the annual Count Plot
figs4a <- sd1 %>%
  ggplot(aes(x = publication_year, y = n)) +
    geom_col(fill = "#1b9e77", alpha = 0.7) +
    geom_text(aes(label = n), position = position_stack(vjust = 0.6), 
              fontface = "bold", color = "white", size = 6, hjust = 0.4) +
    scale_x_continuous(breaks = seq(min(sd1$publication_year), max(sd1$publication_year), by = 1), 
                       expand = expansion(0,1)) +
    scale_y_continuous("Annual Article Count", limits = c(0,15)) +
    labs(x = "Year", tag = "A") +
    theme_count() +
    theme(axis.title.x = element_blank())


# Create the cumulative Count Plot
figs4b <- sd1 %>%
  ggplot(aes(x = publication_year, y = n_cumulative)) +
    geom_line(color = "#7570b3", size = 1, linetype = "solid") +
    geom_point(shape = 21, size = 6, fill = "#7570b3", stroke = 0) +
    geom_text(aes(label = n_cumulative), hjust = 0.2, vjust = -1, size = 6, color = "black") +
    scale_x_continuous(breaks = seq(min(sd1$publication_year), max(sd1$publication_year), by = 1), 
                       expand = expansion(0,1)) +
    scale_y_continuous("Cumulative Article Count", limits = c(0,120)) +
    labs(x = "Year", tag = "B") +
    theme_count()

# Combine the two plots
figs4 <- figs4a / figs4b
figs4

# ggsave(here("figures", "figs4.pdf"), width = 16, height = 12, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs4.jpg"), width = 16, height = 12, units = "cm", scale = 2, dpi = 800)

```

# Section 1
To evaluate the methodological quality and patterns of existing meta-analyses studying the effects of organochlorine pesticides. 

## Figure 2
The methodological and reporting quality of meta-analyses according to CEESAT v. 2.1 (Woodcock et al., 2014). Scores are represented by the following colours: gold is regarded as the highest (best) score, green is second highest score, amber is second-lowest score, and red is the lowest (worst) score. The total counts of studies allocated to each score are shown in each bar. All CEESAT v. 2.1 items, along with our interpretation, are provided in Supplementary File 2. 


## Figure 2a
CEESAT scores for 83 assessed meta-analyses 
```{r, fig.width=25, fig.height=15}
# Start the data manipulation
percent_ceesat_score <- sd %>%
  filter(!is.na(author_year)) %>%
  select(studies = author_year, starts_with("CEE")) %>%
  na.omit() %>%
  pivot_longer(cols = -studies, names_to = "question", values_to = "score") %>%
  group_by(question, score) %>%
  summarise(n = n(), .groups = 'drop') %>%
  mutate(percent = (n/sum(n))*100, 
         across(c(question, score), as.factor),
         question = fct_recode(question, 
           `1.1 Are the elements of the review question clear?` = "CEESAT2_1.1",
           `2.1 Is there an a-priori method protocol document?` = "CEESAT2_2.1",
           `3.1. Is the approach to searching clearly defined systematic and\ntransparent?` = "CEESAT2_3.1",
           `3.2. Is the search comprehensive?` = "CEESAT2_3.2",
           `4.1. Are eligibility criteria clearly defined?` = "CEESAT2_4.1",
           `4.2. Are eligibility criteria consistently applied to all potentially relevant\narticles and studies found during the search?` = "CEESAT2_4.2",
           `4.3. Are eligibility decisions transparently reported?` = "CEESAT2_4.3",
           `5.1. Does the review critically appraise each study?` = "CEESAT2_5.1",
           `5.2. During critical appraisal was an effort made to minimise\nsubjectivity?` = "CEESAT2_5.2",
           `6.1. Is the method of data extraction fully documented?` = "CEESAT2_6.1",
           `6.2. Are the extracted data reported for each study?` = "CEESAT2_6.2",
           `6.3. Were extracted data cross checked by more than one reviewer?` = "CEESAT2_6.3",
           `7.1. Is the choice of synthesis approach appropriate?` = "CEESAT2_7.1",
           `7.2. Is a statistical estimate of pooled effect provided together with\nmeasure of variance and heterogeneity among studies?` = "CEESAT2_7.2",
           `7.3 Is variability in the study findings investigated and discussed?` = "CEESAT2_7.3",
           `8.1 Have the authors considered limitations in the synthesis?` = "CEESAT2_8.1"),
         question = factor(question, levels = rev(levels(question))),
         score = factor(score, levels = levels(score)[c(4,1,3,2)]))

# Create CEESAT plot 
fig2a <- ggplot(data = percent_ceesat_score, aes(x = question, y = percent, fill = score)) +
  geom_col(width = 0.7, position = "fill", color = "black") +
  geom_text(aes(label = n), position = position_fill(vjust = 0.5), size = 7, fontface = "bold") +
  coord_flip() +
  guides(fill = guide_legend(reverse = TRUE)) +
  scale_fill_manual(values = c("#FF0000","#FFD700","#008000", "#DAA520"), name = "Score:") +
  scale_y_continuous(labels = scales::percent) +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        panel.background = element_blank(),
        axis.text.y = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.title.x = element_text(size = 25),
        axis.title.y = element_text(size = 25), 
        legend.position = "top",  
        legend.justification = "left",  
        legend.direction = "horizontal",
        legend.box = "horizontal",  
        legend.box.just = "left",  
        legend.title.align = 0.5,  
        legend.key.size = unit(1, "cm"),  
        legend.title = element_text(size =25),
        legend.text = element_text(size = 25),  
        strip.text = element_text(size = 20),
        strip.background = element_blank(), 
        plot.tag = element_text(size = 25)) + 
  ylab("Percentage") + 
  xlab("CEESAT Question") +  
  labs(tag = "A")

fig2a

total_n <- sum(percent_ceesat_score$n)
percentages <- tibble(
  red_percentage = sum(percent_ceesat_score$n[percent_ceesat_score$score == "Red"]) / total_n * 100,
  amber_percentage = sum(percent_ceesat_score$n[percent_ceesat_score$score == "Amber"]) / total_n * 100,
  green_percentage = sum(percent_ceesat_score$n[percent_ceesat_score$score == "Green"]) / total_n * 100,
  gold_percentage = sum(percent_ceesat_score$n[percent_ceesat_score$score == "Gold"]) / total_n * 100
)

 percentages

# ggsave(here("figures", "fig2a.pdf"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "fig2a.jpg"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)
```

## 
Making the CEESAT question groups for result reporting
```{r}
# Create new categories for specified questions in numerical order
percent_ceesat_score$combined_category <- ifelse(
  grepl("^1\\.1", percent_ceesat_score$question) | grepl("^2\\.1", percent_ceesat_score$question),
  "Study Question",
  ifelse(grepl("^3\\.1", percent_ceesat_score$question) | grepl("^3\\.2", percent_ceesat_score$question),
    "Literature Searching",
    ifelse(grepl("^4\\.1", percent_ceesat_score$question) | grepl("^4\\.2", percent_ceesat_score$question) | grepl("^4\\.3", percent_ceesat_score$question),
      "Literature Screening",
      ifelse(grepl("^5\\.1", percent_ceesat_score$question) | grepl("^5\\.2", percent_ceesat_score$question) | 
             grepl("^6\\.1", percent_ceesat_score$question) | grepl("^6\\.2", percent_ceesat_score$question) | grepl("^6\\.3", percent_ceesat_score$question),
        "Data Extraction", 
        ifelse(grepl("^7", percent_ceesat_score$question), "Data Analysis", 
          ifelse(grepl("^8", percent_ceesat_score$question), "Study Limitations", "Other"))))))

# Group by combined_category and score, then summarize
percent_ceesat_score_grouped <- percent_ceesat_score %>%
  group_by(combined_category, score) %>%
  summarise(total_n = sum(n))

# Calculate the total count for each special category
total_counts <- percent_ceesat_score_grouped %>%
  group_by(combined_category) %>%
  summarise(total_count = sum(total_n))

# Join total counts with grouped data
percent_ceesat_score_grouped <- left_join(percent_ceesat_score_grouped, total_counts, by = "combined_category")

# Add a column for relative percent
percent_ceesat_score_grouped <- percent_ceesat_score_grouped %>%
  mutate(relative_percent = (total_n / total_count) * 100)

print(percent_ceesat_score_grouped)
# view(percent_ceesat_score_grouped)
```

## 
Calculating altmetric for each study 
```{r}
# load data
bib_alt <- read_csv(here("data","scopus.csv")) 

# function getAltmetrics(), the only parameter is doi; see example below
getAltmetrics <- function(doi = NULL,
                          foptions = list(),
                           ...) {
    if (!is.null(doi)) doi <- stringr::str_c("doi/", doi)
    identifiers <- purrr::compact(list(doi))
    if (!is.null(identifiers)) {
      ids <- identifiers[[1]]
    }
    base_url <- "http://api.altmetric.com/v1/"
    #request <- httr::GET(paste0(base_url, ids), httr::add_headers("user-agent" = "#rstats rAltmertic package https://github.com/ropensci/rAltmetric"))
    request <- httr::GET(paste0(base_url, ids))
    results <-
      jsonlite::fromJSON(httr::content(request, as = "text"), flatten = TRUE)
    results <- rlist::list.flatten(results)
    class(results) <- "altmetric"
    results
}
altmetric.crawler <- list(NULL)
for (n in 1:length(bib_alt$DOI)) {
 # format altmetric object
  format.Altmetric <- function(altmetric.object) {
  stats <- altmetric.object[grep("^cited", names(altmetric.object))]
  stats <- data.frame(stats, stringsAsFactors = FALSE)
  data.frame(paper_title = altmetric.object$title,
             journal = altmetric.object$journal,
             doi = altmetric.object$doi,
             #subject = altmetric.object$subjects,
             Altmetric.score = altmetric.object$score,
             stats = stats)
}
   # JASON formate
  altmetric.crawler[[n]]  <-  try(list(format.Altmetric(getAltmetrics(doi = bib_alt$DOI[n])))) # https://stackoverflow.com/questions/14059657/how-to-skip-an-error-in-a-loop?rq=1
  
  # create a dataframe function
  altmetric_df <- function(altmetric.object) {
  df <- data.frame(t(unlist(altmetric.object)), stringsAsFactors = FALSE)
  }
  #altmetric.crawler[[n]]  <-  try(list(altmetric_df(getAltmetrics(doi = DOIs[n]))))
  # create a function to summarize Altmetric object
  summary.altmetric <- function(x, ...) {
  if (inherits(x, "altmetric"))  {
string <- "Altmetrics on: \"%s\" with altmetric_id: %s published in %s."
vals   <- c(x$title,  x$altmetric_id, x$journal)
 if("journal" %in% names(x)) {
  cat(do.call(sprintf, as.list(c(string, vals))))
 } else {
   string <- "Altmetrics on: \"%s\" with altmetric_id: %s"
   cat(do.call(sprintf, as.list(c(string, vals))))
 }
  cat("\n")
  stats <- x[grep("^cited", names(x))]
  stats <- data.frame(stats, stringsAsFactors = FALSE)
  print(data.frame(stats = t(stats)))
  }
}
  # crawl
 # altmetric.crawler[[n]] <- try(list(summary.altmetric(getAltmetrics(doi = DOIs[n]))))
}
# save results from altmetric.crawler and retrieve lists within lists
altmetric.crawler2 <- sapply(altmetric.crawler, function(x) {x})
# retrieve stats
altmetric.summary <- data.frame(paper_title = sapply(altmetric.crawler2, function(x)  ifelse(class(x) == "data.frame",x$paper_title,NA)),
           journal = sapply(altmetric.crawler2, function(x) ifelse(class(x) == "data.frame",x$journal,NA)),
           doi = sapply(altmetric.crawler2, function(x) ifelse(class(x) == "data.frame",x$doi,NA)),
           #subject = sapply(altmetric.crawler2, function(x) ifelse(class(x) == "data.frame",x$subject,NA)),
           Altmetric.score = sapply(altmetric.crawler2, function(x) ifelse(class(x) == "data.frame",x$Altmetric.score,0)),
           policy = sapply(altmetric.crawler2, function(x) ifelse(class(x) == "data.frame",ifelse(!is.null(x$stats.cited_by_policies_count),x$stats.cited_by_policies_count,0),0)),
           patent = sapply(altmetric.crawler2, function(x) ifelse(class(x) == "data.frame",ifelse(!is.null(x$stats.cited_by_patents_count),x$stats.cited_by_patents_count,0),0))
           )
# Adding new columns to bib_alt dataframe
bib_alt <- bib_alt %>%
   mutate(Altmetric.score = altmetric.summary$Altmetric.score, 
          policy = altmetric.summary$policy, 
          patent = altmetric.summary$patent)

# Defining the scoring function
score <- function(x) {
  case_when(
    x == 'Red' ~ 0,
    x == 'Amber' ~ 1,
    x == 'Green' ~ 3,
    x == 'Gold' ~ 4,
    TRUE ~ NA_real_
  )
}
# Apply the scoring function across all columns starting with "CEESAT"
sd <- sd %>%
  mutate(across(starts_with("CEESAT"), score, .names = "points_{.col}"))
# Sum all the points columns to create a 'total_points' column
sd <- sd %>%
  mutate(total_points = rowSums(select(., starts_with("points_")), na.rm = TRUE))

```

## Figure 2b
CEESAT scores for meta-analyses cited in policy documents (left panel) and those not cited in policy documents (right panel). 
```{r, fig.width=25, fig.height=15}
sd_bib_alt <- sd %>%
  left_join(bib_alt, by = "DOI")

# Converting policy to binary and dropping NAs
sd_bib_alt_policy <- sd_bib_alt %>%
  mutate(binary_policy = if_else(policy != 0, "Cited in Policy", "Not Cited in Policy")) %>%
  drop_na(binary_policy, total_points)

# Converting policy to binary and dropping NAs
sd_bib_alt_policy <- sd_bib_alt %>%
  mutate(binary_policy = if_else(policy != 0, "Cited in Policy", "Not Cited in Policy")) %>%
  drop_na(binary_policy, total_points)
percent_ceesat_score1 <- sd_bib_alt_policy %>%
  filter(!is.na(author_year)) %>%
  select(studies = author_year, binary_policy, starts_with("CEE")) %>%
  na.omit() %>%
  pivot_longer(cols = -c(studies, binary_policy), names_to = "question", values_to = "score") %>%
  group_by(binary_policy, question, score) %>%
  summarise(n = n(), .groups = 'drop') %>%
  mutate(percent = (n/sum(n))*100, 
         across(c(question, score), as.factor),
         question = fct_recode(question, 
           `1.1` = "CEESAT2_1.1",
           `2.1` = "CEESAT2_2.1",
           `3.1` = "CEESAT2_3.1",
           `3.2` = "CEESAT2_3.2",
           `4.1` = "CEESAT2_4.1",
           `4.2` = "CEESAT2_4.2",
           `4.3` = "CEESAT2_4.3",
           `5.1` = "CEESAT2_5.1",
           `5.2` = "CEESAT2_5.2",
           `6.1` = "CEESAT2_6.1",
           `6.2` = "CEESAT2_6.2",
           `6.3` = "CEESAT2_6.3",
           `7.1` = "CEESAT2_7.1",
           `7.2` = "CEESAT2_7.2",
           `7.3` = "CEESAT2_7.3",
           `8.1` = "CEESAT2_8.1"),
         question = factor(question, levels = rev(levels(question))),
         score = factor(score, levels = levels(score)[c(4,1,3,2)]))


fig2b <- ggplot(data = percent_ceesat_score1, aes(x = question, y = percent, fill = score)) +
  geom_col(width = 0.7, position = "fill", color = "black") +
  geom_text(aes(label = n), position = position_fill(vjust = 0.5), size = 7, fontface = "bold") +
  coord_flip() +
  guides(fill = guide_legend(reverse = TRUE)) +
  scale_fill_manual(values = c("#FF0000","#FFD700","#008000", "#DAA520"), name = "Score:") +
  scale_y_continuous(labels = scales::percent) +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        panel.background = element_blank(),
        axis.text.y = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.title.x = element_text(size = 25),
        axis.title.y = element_text(size = 25), 
        legend.position = "none",
        strip.text = element_text(size = 20),
        strip.background = element_blank(), 
        plot.tag = element_text(size = 25)) + 
  ylab("Percentage") + 
  xlab("CEESAT Question") +
  facet_wrap(~binary_policy) +
  labs(tag = "B")

fig2b
# ggsave(here("figures", "fig2b.pdf"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "fig2b.jpg"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)


policy <- sd_bib_alt_policy$total_points[sd_bib_alt_policy$binary_policy == "Cited in Policy"]
not_policy <- sd_bib_alt_policy$total_points[sd_bib_alt_policy$binary_policy == "Not Cited in Policy"]

# QQ Plot
qqnorm(policy)
qqline(policy, col = "red")

# QQ Plot
qqnorm(not_policy)
qqline(not_policy, col = "red")

t.test(policy,not_policy)

```

## Figure s5
CEESAT scores for meta-analyses referencing a reporting guideline (right panel) and those not referencing a reporting guideline (left panel).
```{r, fig.width=25, fig.height=15}
sd_bib_alt_guideline <- sd_bib_alt %>% 
  mutate(binary_guideline = if_else(reporting_standards_type == "Not reported", "Not reported", "Reported")) %>% 
    drop_na(binary_guideline, total_points)

percent_ceesat_score2 <- sd_bib_alt_guideline %>%
  filter(!is.na(author_year)) %>%
  select(studies = author_year, binary_guideline, starts_with("CEE")) %>%
  na.omit() %>%
  pivot_longer(cols = -c(studies, binary_guideline), names_to = "question", values_to = "score") %>%
  group_by(binary_guideline, question, score) %>%
  summarise(n = n(), .groups = 'drop') %>%
  mutate(percent = (n/sum(n))*100, 
         across(c(question, score), as.factor),
         question = fct_recode(question, 
           `1.1` = "CEESAT2_1.1",
           `2.1` = "CEESAT2_2.1",
           `3.1` = "CEESAT2_3.1",
           `3.2` = "CEESAT2_3.2",
           `4.1` = "CEESAT2_4.1",
           `4.2` = "CEESAT2_4.2",
           `4.3` = "CEESAT2_4.3",
           `5.1` = "CEESAT2_5.1",
           `5.2` = "CEESAT2_5.2",
           `6.1` = "CEESAT2_6.1",
           `6.2` = "CEESAT2_6.2",
           `6.3` = "CEESAT2_6.3",
           `7.1` = "CEESAT2_7.1",
           `7.2` = "CEESAT2_7.2",
           `7.3` = "CEESAT2_7.3",
           `8.1` = "CEESAT2_8.1"),
         question = factor(question, levels = rev(levels(question))),
         score = factor(score, levels = levels(score)[c(4,1,3,2)]))

figs5 <- ggplot(data = percent_ceesat_score2, aes(x = question, y = percent, fill = score)) +
  geom_col(width = 0.7, position = "fill", color = "black") +
  geom_text(aes(label = n), position = position_fill(vjust = 0.5), size = 7, fontface = "bold") +
  coord_flip() +
  guides(fill = guide_legend(reverse = TRUE)) +
  scale_fill_manual(values = c("#FF0000","#FFD700","#008000", "#DAA520"), name = "Score:") +
  scale_y_continuous(labels = scales::percent) +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        panel.background = element_blank(),
        axis.text.y = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.title.x = element_text(size = 25),
        axis.title.y = element_text(size = 25), 
        legend.position = "top",  
        legend.justification = "left",  
        legend.direction = "horizontal",
        legend.box = "horizontal",  
        legend.box.just = "left",  
        legend.title.align = 0.5,  
        legend.key.size = unit(1, "cm"),  
        legend.title = element_text(size =25),
        legend.text = element_text(size = 25),  
        strip.text = element_text(size = 20),
        strip.background = element_blank()) +  
  ylab("Percentage") + 
  xlab("CEESAT Question") +
  facet_wrap(~binary_guideline) 

figs5
# ggsave(here("figures", "figs5.pdf"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs5.jpg"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)

guidline <- sd_bib_alt_guideline$total_points[sd_bib_alt_guideline$binary_guideline == "Reported"]
no_guideline <- sd_bib_alt_guideline$total_points[sd_bib_alt_guideline$binary_guideline == "Not reported"]

# QQ Plot
 qqnorm(guidline)
 qqline(guidline, col = "red")

# QQ Plot
 qqnorm(no_guideline)
 qqline(no_guideline, col = "red")

t.test(guidline,no_guideline)


```

## Figure s6 
Scatter plot showing the relationship between the quantity of times cited in a policy document and total CEESAT score 
```{r, fig.width=16, fig.height=12}
# Create the plot 
figs6 <- sd_bib_alt_policy %>%
 # filter(total_points != 0, policy != 0) %>% 
  ggplot(aes(x = total_points, y = policy)) +
  geom_point(color = "#1b9e77", alpha = 0.8, size = 8) +
  geom_smooth(method = lm, se = TRUE, color = "red") +
  labs(x = "Total CEESAT score", y = "Policy Count") +
  theme_minimal() +
  theme(panel.grid.major.y = element_blank(),
        axis.line.y = element_blank(),
        axis.ticks.y = element_blank(),
        axis.text.x = element_text(size = 25),
        axis.text.y = element_text(size = 25),
        axis.title.x = element_text(size = 30),
        axis.title.y = element_text(size = 30),
        plot.title = element_blank())

figs6

# ggsave(here("figures", "figs6.pdf"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs6.jpg"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)
```


## Figure s7
Box and violin plot showing the distribution of CEESAT scores for studies cited in policy documents and studies not cited in policy documents
```{r, fig.width=25, fig.height=15}
figs7 <- sd_bib_alt_policy %>%
  ggplot(aes(x = binary_policy, y = total_points)) +
  geom_violin(fill = "#1b9e77", alpha =0.2, color = NA, trim = FALSE) +
  geom_boxplot(width = 0.05, fill = "white", color = "#1b9e77", outlier.shape = NA) +
  geom_jitter(width = 0.1, height = 0.05, color = "#1b9e77", alpha = 0.8, size = 6) +
  scale_y_continuous(limits = c(0, max(sd_bib_alt$total_points))) +
  labs( x = "Policy", y = "Total CEESAT Score") +
  theme_minimal() +
  theme(panel.grid.major.y = element_blank(),
        axis.line.y = element_blank(),
        axis.ticks.y = element_blank(),
        axis.text.x = element_text(size = 25),
        axis.text.y = element_text(size = 25),
        axis.title.x = element_text(size = 30),
        axis.title.y = element_text(size = 30),
        plot.title = element_blank())

figs7

# ggsave(here("figures", "figs7.pdf"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs7.jpg"), width = 25, height = 15, units = "cm", scale = 2, dpi = 800)
```

## Figure 3a
Bar plot showing the percentage and total count of bias methodologies used in meta-analyses investigating the impacts of organochlorine pesticides. Note that some meta-analyses may contribute to multiple sections if the study involved the use of multiple bias assessment methodologies. 
```{r, fig.width=16, fig.height=10}
# Calculate the total count for each category
bias_method_count <- sd %>% 
  separate_rows(bias_assessment_method, sep = ",\\s+") %>% 
  count(bias_assessment_method) %>%
  filter(bias_assessment_method != "NA") %>%
    group_by(bias_assessment_method) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
bias_method_pct <- bias_method_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
fig3a <- bias_method_count %>%
  ggplot(aes(x = n, y = reorder(bias_assessment_method, n), fill = ifelse(grepl("Not reported", bias_assessment_method), "#7370b3", "#1b9e77"))) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(bias_assessment_method, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = bias_method_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(bias_method_count$n)*1.3)) +
  labs(title = "Publication Bias Count", x = NULL, y = NULL, tag = "A", size = 30) +
  organochlorTHEME() 

fig3a

# ggsave(here("figures", "fig3h.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "fig3h.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
```

## Figure s8
Alluvial plot showing the relationship between the Journal Citation Report Category and bias assessment method.
```{r, fig.width=16, fig.height=10}
# Data Transformation 
bias_assessment_alluvial <- sd %>% 
    separate_rows(bias_assessment_method, sep = ",\\s+") %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>% 
    filter(!is.na(bias_assessment_method)) %>%
    group_by(Journal_Category_Allocated_Broad, bias_assessment_method) %>% 
    count(bias_assessment_method, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(bias_assessment_method)

# Create the Alluvial plot
figs8 <- bias_assessment_alluvial %>% 
  ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = bias_assessment_method)) +
  scale_x_discrete(limits = c("Journal Citation Category", "Bias Assessment"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/3.5, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 6) +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs8

# ggsave(here("figures", "figs8.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs8.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

```

## Figure 3b
Bar plot showing the percentage and total count of heterogeneity assessment methods used in meta-analyses investigating the impacts of organochlorine pesticides. Note that some meta-analyses may contribute to multiple sections if the study involved multiple heterogeneity assessments. Percentage is showing the proportion of studies.
```{r, fig.width=16, fig.height=10}
# Calculate the total count for each category
heterogeneity_count <- sd %>% 
  separate_rows(heterogeneity_assessment_method, sep = ",\\s+") %>% 
  count(heterogeneity_assessment_method) %>%
  filter(heterogeneity_assessment_method != "NA") %>%
    group_by(heterogeneity_assessment_method) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
heterogeneity_pct <- heterogeneity_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
fig3b <- heterogeneity_count %>%
  ggplot(aes(x = n, y = reorder(heterogeneity_assessment_method, n), fill = ifelse(grepl("Not reported", heterogeneity_assessment_method), "#7370b3", "#1b9e77"))) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(heterogeneity_assessment_method, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = heterogeneity_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(heterogeneity_count$n)*1.3)) +
  labs(title = "Heterogeneity Count", x = NULL, y = NULL, tag = "B", size = 30) +
  organochlorTHEME() 

fig3b

# ggsave(here("figures", "fig3b.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "fig3b.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

```
 
## Figure s9 
Alluvial plot showing the relationship between the Journal Citation Report Category and heterogeneity assessment method.
```{r, fig.width=16, fig.height=10}
# Data Transformation 
heterogeneity_alluvial <- sd %>% 
    separate_rows(heterogeneity_assessment_method, sep = ",\\s+") %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>% 
    filter(!is.na(heterogeneity_assessment_method)) %>%
    group_by(Journal_Category_Allocated_Broad, heterogeneity_assessment_method) %>% 
    count(heterogeneity_assessment_method, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(heterogeneity_assessment_method) 

# Create the Alluvial plot
figs9 <- heterogeneity_alluvial %>% 
ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = heterogeneity_assessment_method)) +
  scale_x_discrete(limits = c("Journal Citation Category", "Heterogeneity"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/4.5, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 6) +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs9

# ggsave(here("figures", "figs9.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs9.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
```
 
## Figure 3c
Bar plot showing the percentage and total count of sensitivity analyses conducted in meta-analyses investigating the impacts of organochlorine pesticides. Note that some meta-analyses may contribute to multiple sections if the study involved multiple sensitivity analyses.
```{r, fig.width=16, fig.height=10}
# Calculate the total count for each category
sensitivity_count <- sd %>% 
  separate_rows(sensitivity_analysis_method, sep = ",\\s+") %>% 
  count(sensitivity_analysis_method) %>%
  filter(sensitivity_analysis_method != "NA") %>%   
  group_by(sensitivity_analysis_method) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
sensitivity_pct <- sensitivity_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
fig3c <- sensitivity_count %>%
  ggplot(aes(x = n, y = reorder(sensitivity_analysis_method, n), fill = ifelse(grepl("Not reported", sensitivity_analysis_method), "#7370b3", "#1b9e77"))) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(sensitivity_analysis_method, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = sensitivity_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(sensitivity_count$n)*1.3)) +
  labs(title = "Sensitivity Analysis Count", x = NULL, y = NULL, tag = "C", size = 30) +
  organochlorTHEME() 

fig3c

# ggsave(here("figures", "fig3c.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "fig3c.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

```

## Figure s10
Alluvial plot showing the relationship between the Journal Citation Report Category and sensitivity analysis.
```{r, fig.width=16, fig.height=10}
# Data Transformation 
sensitivity_analysis_alluvial <- sd %>% 
    separate_rows(sensitivity_analysis_method, sep = ",\\s+") %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>% 
    filter(!is.na(sensitivity_analysis_method)) %>%
    group_by(Journal_Category_Allocated_Broad, sensitivity_analysis_method) %>% 
    count(sensitivity_analysis_method, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(sensitivity_analysis_method)

# Create the Alluvial plot
figs10 <- sensitivity_analysis_alluvial %>% 
ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = sensitivity_analysis_method)) +
  scale_x_discrete(limits = c("Journal Citation Category", "Sensitivity Analysis"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/4, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 5) +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs10

# ggsave(here("figures", "figs10.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs10.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

```

## Figure 3d
Bar plot showing the percentage and total count of reporting guidelines used in meta-analyses investigating the impacts of organochlorine pesticides. Note that some meta-analyses may contribute to multiple sections if the study involved the use of multiple reporting guidelines.
```{r, fig.width=16, fig.height=10}
# Calculate the total count for each category
reporting_guide_count <- sd %>% 
  separate_rows(reporting_standards_type, sep = ",\\s+") %>% 
  count(reporting_standards_type) %>%
  filter(reporting_standards_type != "NA") %>%
  mutate(reporting_standards_type = ifelse(n < 3, "Other Guidelines", as.character(reporting_standards_type))) %>%
  group_by(reporting_standards_type) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
reporting_guide_pct <- reporting_guide_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
fig3d <- reporting_guide_count %>%
  ggplot(aes(x = n, y = reorder(reporting_standards_type, n), fill = ifelse(grepl("Not reported", reporting_standards_type), "#7370b3", "#1b9e77"))) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(reporting_standards_type, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = reporting_guide_pct, aes(label = paste0("(", round(percentage, 0), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
 scale_x_continuous(expand = c(0, 0), limits = c(0, max(reporting_guide_count$n)*1.3)) +
  labs(title = "Reporting Guideline Count", x = NULL, y = NULL, size = 30) +
  organochlorTHEME() 

fig3d

# ggsave(here("figures", "fig3d.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "fig3d.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
```

## Figure s11
An alluvial plot showing the relationship between the Journal Citation Report Category and reporting guideline used 
```{r, fig.width=16, fig.height=10}
# Data Transformation 
reporting_standards_alluvial <- sd %>% 
    separate_rows(reporting_standards_type, sep = ",\\s+") %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>%
    filter(!grepl("no category found", Journal_Category_Allocated_Broad, ignore.case = TRUE)) %>% 
    filter(!is.na(reporting_standards_type)) %>%
    group_by(Journal_Category_Allocated_Broad, reporting_standards_type) %>% 
    count(reporting_standards_type, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(reporting_standards_type)

# Create the Alluvial plot
figs11 <- reporting_standards_alluvial %>% 
ggplot( aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = reporting_standards_type)) +
  scale_x_discrete(limits = c("Journal Citation Category", "Reporting Standards Type"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/3.5, fill = "white", color = "black") +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 6) +
    scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs11

# ggsave(here("figures", "figs11.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs11.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
```

## Figure 3
```{r, fig.width=36, fig.height=20}

fig3 <- fig3a + fig3b + fig3c + fig3d +
        plot_layout(ncol = 2, nrow = 2)
fig3

# ggsave(here("figures", "fig3.pdf"), width = 36 , height = 20 , units = "cm", scale = 2, dpi = 400)
# ggsave(here("figures", "fig3.jpg"), width = 36 , height = 20, units = "cm", scale = 2, dpi = 400)
```

## Figure s12
Bar plot showing the percentage and total count of scientific literature databases used in meta-analyses investigating the impacts of organochlorine pesticides. Note that some meta-analyses may contribute to multiple sections if the study involved multiple scientific literature databases. The "Other databases" category includes all databases with a count of 3 or less. Percentage is showing the proportion of studies.
```{r, fig.width=16, fig.height=10}
# Calculate the total count for each category
database_count <- sd %>% 
  separate_rows(database_search, sep = ",\\s+") %>% 
  count(database_search) %>% 
  filter(database_search != "NA") %>% 
  arrange(desc(n)) %>% 
  mutate(database_search = ifelse(n<= 3, "Other databases", as.character(database_search))) %>% 
    group_by(database_search) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
database_pct <- database_count %>%
  mutate(proportion = n / 83, # 83 is number of studies assessed for CEESAT
         percentage = proportion * 100)

# Create the count plot
figs12 <- database_count %>%
  ggplot(aes(x = n, y = reorder(database_search, n), fill = "#1b9e77")) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(database_search, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = database_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(database_count$n)*1.3)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME() 

figs12

# ggsave(here("figures", "figs12.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs12.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
```

## Figure s13
Alluvial plot showing the relationship between the Journal Citation Report Category and the scientific literature database used. Filtered for scientific literature database counts greater than or equal to 3. 
```{r, fig.width=16, fig.height=10}
# Rename Environmental Science
sd <- sd %>%
  mutate(Journal_Category_Allocated_Broad = str_replace(Journal_Category_Allocated_Broad, "Environmental Science", "Environmental\nScience"))

# Data Transformation 
database_alluvial <- sd %>% 
    separate_rows(database_search, sep = ",\\s+") %>%
    group_by(Journal_Category_Allocated_Broad, database_search) %>% 
    count(database_search, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(database_search) %>% 
    filter(sum(freq) >= 3) %>% 
    filter(database_search != "NA")

# Create the Alluvial plot
figs13 <- database_alluvial %>% 
ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = database_search)) +
  scale_x_discrete(limits = c("Journal Citation Report", "Database Search"), expand = c(.05, .05)) +
  xlab("Variables") +
  ylab("Frequency") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/4.5, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 6) +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs13

# ggsave(here("figures", "figs13.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs13.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
```

## Figure s14
Bar plot showing the percentage and total count of software for analysis used in meta-analyses investigating the impacts of organochlorine pesticides. Note that some meta-analyses may contribute to multiple sections if the study involved multiple software. Percentage is showing the proportion of studies.
```{r, fig.width=16, fig.height=10}
# Calculate the total count for each category
software_count <- sd %>%
  separate_rows(software_analysis, sep = ",\\s+") %>%
  mutate(software_analysis = ifelse(grepl("comprehensive meta-analysis", software_analysis), "CMAS", software_analysis)) %>%
  count(software_analysis) %>%
  filter(software_analysis != "NA") %>%
  arrange(desc(n)) %>%
  mutate(software_analysis = ifelse(n < 2, "Other Software", as.character(software_analysis))) %>%
  group_by(software_analysis) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
software_pct <- software_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
figs14 <- software_count %>%
  ggplot(aes(x = n, y = reorder(software_analysis, n), fill = "#1b9e77")) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(software_analysis, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = software_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(software_count$n)*1.3)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME() 

figs14

# ggsave(here("figures", "figs14.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs14.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
```

## Figure s15 
Alluvial plot showing the relationship between the Journal Citation Report Category and the software used for analysis.
```{r, fig.width=16, fig.height=10}
# Data Transformation 
software_analysis_alluvial <- sd %>% 
    separate_rows(software_analysis, sep = ",\\s+") %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>% 
  mutate(software_analysis = ifelse(grepl("comprehensive meta-analysis", software_analysis), "CMAS", software_analysis)) %>%
  mutate(software_analysis = ifelse(grepl("not reported", software_analysis), "no software reported", software_analysis))  %>% 
    filter(!is.na(software_analysis)) %>%
    group_by(Journal_Category_Allocated_Broad, software_analysis) %>% 
    count(software_analysis, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(software_analysis)

# Create the Alluvial plot for Software Analysis
figs15 <- software_analysis_alluvial %>% 
  ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = software_analysis)) +
  scale_x_discrete(limits = c("Journal Citation Category", "Software Analysis"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/4, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 6) +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs15

# ggsave(here("figures", "figs15.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs15.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
```

## Figure s16 
Bar plot showing the percentage and total count of effect size calculation types used in meta-analyses investigating the impacts of organochlorine pesticides. Note that some meta-analyses may contribute to multiple sections if the study involved multiple effect size calculations. The "Other effect sizes" category includes all effect sizes with a count of 2 or less. Percentage is showing the proportion of studies.
```{r, fig.width=16, fig.height=10}
# Calculate the total count for each category
effectsize_count <- sd %>% 
  separate_rows(effect_size, sep = ",\\s+") %>% 
  count(effect_size) %>%
  filter(effect_size != "NA") %>%
  mutate(effect_size = ifelse(n<= 2, "Other effect size", as.character(effect_size))) %>% 
  group_by(effect_size) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
effectsize_pct <- effectsize_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
figs16 <-  effectsize_count %>%
  ggplot(aes(x = n, y = reorder(effect_size, n), fill = "#1b9e77")) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(effect_size, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = effectsize_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(effectsize_count$n)*1.3)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME() 

figs16

# ggsave(here("figures", "figs16.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs16.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
```

## Figure s17
Alluvial plot showing the relationship between the Journal Citation Report Category and the effect size used. Filtered for scientific literature database counts greater than or equal to 3.
```{r, fig.width=16, fig.height=10}
# Data Transformation 
effectsize_alluvial <- sd %>% 
    separate_rows(effect_size, sep = ",\\s+") %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>% 
    filter(!grepl("no category found", Journal_Category_Allocated_Broad, ignore.case = TRUE)) %>% 
    filter(!is.na(effect_size)) %>%
    group_by(Journal_Category_Allocated_Broad, effect_size) %>% 
    count(effect_size, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(effect_size) 


# Create the Alluvial plot
figs17 <- effectsize_alluvial %>% 
ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = effect_size)) +
  scale_x_discrete(limits = c("Journal Citation Category", "Effect Size"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/2.5, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 5) +
  theme_minimal() +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 
  
figs17

# ggsave(here("figures", "figs17.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs17.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
```

## Figure s18
Bar plot showing the percentage and total count of visualization methods used in meta-analyses investigating the impacts of organochlorine pesticides. Note that some meta-analyses may contribute to multiple sections if the study involved the use of multiple visualization methods.
```{r, fig.width=16, fig.height=10}
# Calculate the total count for each category
visualization_count <- sd %>% 
  separate_rows(visualization_method, sep = ",\\s+") %>% 
  count(visualization_method) %>%
  filter(visualization_method != "NA") %>%
    group_by(visualization_method) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
visualization_pct <- visualization_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
figs18 <- visualization_count %>%
  ggplot(aes(x = n, y = reorder(visualization_method, n), fill = ifelse(grepl("Not reported", visualization_method), "#7370b3", "#1b9e77"))) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(visualization_method, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = visualization_pct, aes(label = paste0("(", round(percentage, 0), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(visualization_count$n)*1.3)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME() 

figs18

# ggsave(here("figures", "figs18.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs18.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
```

## Figure s19
Alluvial plot showing the relationship between the Journal Citation Report Category and visualization method. 
```{r, fig.width=16, fig.height=10}
# Data Transformation 
visualization_alluvial <- sd %>% 
    separate_rows(visualization_method, sep = ",\\s+") %>%
    filter(!is.na(visualization_method)) %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>%
    group_by(Journal_Category_Allocated_Broad, visualization_method) %>% 
    count(visualization_method, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(visualization_method) 


# Create the Alluvial plot
figs19 <- visualization_alluvial %>% 
ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = visualization_method)) +
  scale_x_discrete(limits = c("Journal Citation Report Category", "ROB Assessment Method"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/4, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 6) +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs19

# ggsave(here("figures", "figs19.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs19.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
```

## Figure s20
Bar plot showing the percentage and total count of risk of bias tests used in meta-analyses investigating the impacts of organochlorine pesticides. Note that some meta-analyses may contribute to multiple sections if the study involved the use of multiple risk of bias tests. 
```{r, fig.width=16, fig.height=10}
# Calculate the total count for each category
rob_method_count <- sd %>% 
  separate_rows(rob_assessment_method, sep = ",\\s+") %>% 
  count(rob_assessment_method) %>%
  filter(rob_assessment_method != "NA") %>%
  mutate(rob_assessment_method = ifelse(n<= 2, "Other ROB tool", as.character(rob_assessment_method))) %>% 
  mutate(rob_assessment_method = ifelse(grepl("Newcastle Ottawa scale", rob_assessment_method), "Newcastle Ottawa\nscale", rob_assessment_method)) %>%
  group_by(rob_assessment_method) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
rob_method_pct <- rob_method_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
figs20 <- rob_method_count %>%
  ggplot(aes(x = n, y = reorder(rob_assessment_method, n), fill = ifelse(grepl("Not reported", rob_assessment_method), "#7370b3", "#1b9e77"))) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(rob_assessment_method, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = rob_method_pct, aes(label = paste0("(", round(percentage, 0), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(rob_method_count$n)*1.3)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME() 

figs20

# ggsave(here("figures", "figs20.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs20.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
```

## Figure s21
Alluvial plot showing the relationship between the Journal Citation Report Category and risk of bias methodology. 
```{r, fig.width=16, fig.height=10}
# Data Transformation 
robmethod_alluvial <- sd %>% 
     separate_rows(rob_assessment_method, sep = ",\\s+") %>%
    filter(!is.na(rob_assessment_method)) %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>%
    group_by(Journal_Category_Allocated_Broad, rob_assessment_method) %>% 
    count(rob_assessment_method, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(rob_assessment_method) 


# Create the Alluvial plot
figs21 <- robmethod_alluvial %>% 
ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = rob_assessment_method)) +
  scale_x_discrete(limits = c("Journal Citation Category", "ROB Assessment Method"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/3, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 5) +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs21

# ggsave(here("figures", "figs21.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs21.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
```


## Figure s22
Bar plot showing the percentage and total count of bias visualizations used in meta-analyses investigating the impacts of organochlorine pesticides. Note that some meta-analyses may contribute to multiple sections if the study involved the use of multiple bias visualizations. 
```{r, fig.width=16, fig.height=10}
# Calculate the total count for each category
bias_visualization_count <- sd %>% 
  separate_rows(bias_assessment_visualization, sep = ",\\s+") %>% 
  count(bias_assessment_visualization) %>%
  filter(bias_assessment_visualization != "NA") %>%
    group_by(bias_assessment_visualization) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
bias_visualization_pct <- bias_visualization_count %>%
  mutate(proportion = n / 83,
         percentage = proportion * 100)

# Create the count plot
figs22 <- bias_visualization_count %>%
  ggplot(aes(x = n, y = reorder(bias_assessment_visualization, n), fill = ifelse(grepl("Not reported", bias_assessment_visualization), "#7370b3", "#1b9e77"))) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(bias_assessment_visualization, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = bias_visualization_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(expand = c(0, 0), limits = c(0, max(bias_visualization_count$n)*1.3)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME() 

figs22

# ggsave(here("figures", "figs22.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs22.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
```

## Figure s23
Alluvial plot showing the relationship between the Journal Citation Report Category and bias visualization method.
```{r, fig.width=16, fig.height=10}
# Data Transformation 
bias_vizualisation_alluvial <- sd %>% 
    separate_rows(bias_assessment_visualization, sep = ",\\s+") %>%
    separate_rows(Journal_Category_Allocated_Broad, sep = "/\\s+") %>% 
    filter(!is.na(bias_assessment_visualization)) %>%
    group_by(Journal_Category_Allocated_Broad, bias_assessment_visualization) %>% 
    count(bias_assessment_visualization, Journal_Category_Allocated_Broad) %>% 
    summarise(freq = n(), .groups = 'drop') %>% 
    group_by(bias_assessment_visualization)

# Create the Alluvial plot
figs23 <- bias_vizualisation_alluvial %>% 
  ggplot(aes(y = freq ,axis1 = Journal_Category_Allocated_Broad, axis2 = bias_assessment_visualization)) +
  scale_x_discrete(limits = c("Journal Citation Category", "Bias Vizualization"), expand = c(.05, .05)) +
  xlab("Variables") +
  geom_alluvium(aes(fill = Journal_Category_Allocated_Broad)) +
  geom_stratum(width = 1/4, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 6) +
  labs(x= "Variables", y = "Frequency", fill = "Journal Category Allocated") +
  scale_fill_brewer(palette = "Dark2") +
  organochlorTHEME() 

figs23

# ggsave(here("figures", "figs23.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs23.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

```


## Figure s24
A circular treemap showing the counts of each methodological item in existing meta-analysis investigating the impacts of organochlorine pesticides
```{r, fig.width=21, fig.height=15}
# Grouping "Medline" and "Pubmed" under "PubMed" and summing the counts
database_count <- database_count %>%
  mutate(database_search = if_else(database_search %in% c("Medline", "Pubmed"), "PubMed", database_search)) %>%
  group_by(database_search) %>%
  summarise(n = sum(n))

# Splitting, grouping, and summing different categories of effect sizes
effectsize_count <- sd %>% 
  separate_rows(effect_size, sep = ",\\s+") %>% 
  count(effect_size) %>%
  filter(effect_size != "NA") %>%
  group_by(effect_size) %>%
  summarise(n = sum(n))

effectsize_count <- effectsize_count %>% 
  mutate(effect_size = if_else(effect_size %in% c("Beta regression", "Correlation coefficient"), "Correlation", effect_size)) %>% 
  mutate(effect_size = if_else(effect_size %in% c("Odds ratio", "Response ratio", "Standardized mean difference", "Log partitioning ratio", "Log odds ratio", "Ratio of means", "Log response ratio"), "Mean difference", effect_size)) %>% 
  mutate(effect_size = if_else(effect_size %in% c("Risk ratio"), "2x2", effect_size)) %>% 
  mutate(effect_size = if_else(effect_size %in% c("Raw weight", "Geometric mean", "Maternal transfer ratio", "Standardized mortality rate", "Transfer rate","Transfer ratio", "Z-score", "Log coefficient variation ratio"), "Other ES", effect_size)) %>% 
  group_by(effect_size) %>%
  summarise(n = sum(n))

# Categorizing software as either "Code-based software" or "GUI" and summing the counts
software_count <- software_count %>%
  mutate(software_analysis = if_else(software_analysis %in% c("Stata", "R"), "Code-based", software_analysis)) %>%
  mutate(software_analysis = if_else(software_analysis %in% c("CMAS", "Excel", "RevMan", "SAS", "XLSTAT"), "GUI", software_analysis)) %>% 
  mutate(software_analysis = if_else(software_analysis %in% c("Not reported"), "No software reported", software_analysis)) %>%
  group_by(software_analysis) %>%
  summarise(n = sum(n))

# Grouping heterogeneity assessment methods and summing the counts
heterogeneity_count <- heterogeneity_count %>% 
  mutate(heterogeneity_assessment_method = if_else(heterogeneity_assessment_method %in% c("Tau square"), "I squared", heterogeneity_assessment_method)) %>% 
  mutate(heterogeneity_assessment_method = if_else(heterogeneity_assessment_method %in% c("Chi square"), "Q statistic", heterogeneity_assessment_method)) %>% 
  mutate(heterogeneity_assessment_method = if_else(heterogeneity_assessment_method %in% c("Not reported"), "No heterogeneity reported", heterogeneity_assessment_method)) %>% 
  group_by(heterogeneity_assessment_method) %>%
  summarise(n = sum(n))

# Grouping bias assessment methods with count <= 3 under "Other BA" and summing the counts
bias_method_count <- bias_method_count %>% 
  mutate(bias_assessment_method = if_else(bias_assessment_method %in% c("Egger's regression test", "Fail safe", "Begg's test", "Kendall's tau statistic"),"Bias statistical test", bias_assessment_method)) %>% 
    mutate(bias_assessment_method = if_else(bias_assessment_method %in% c("Not reported"),"No bias assessment reported", bias_assessment_method)) %>% 
  group_by(bias_assessment_method) %>%
  summarise(n = sum(n))

# Grouping sensitivity analysis methods and summing the counts
sensitivity_count <-  sensitivity_count %>%  
  mutate(sensitivity_analysis_method = if_else(n<= 8, "Other sensitivity analysis", as.character(sensitivity_analysis_method))) %>% 
   mutate(sensitivity_analysis_method = if_else(sensitivity_analysis_method %in% c("Not reported"), "No sensitivity analysis reported",sensitivity_analysis_method)) %>% 
  group_by(sensitivity_analysis_method) %>%
  summarise(n = sum(n))

# Grouping risk of bias assessment methods with count <= 3 under "Other ROB" and summing the counts
rob_method_count <-  rob_method_count %>%  
  mutate(rob_assessment_method = if_else(n<= 3, "Other ROB tool", as.character(rob_assessment_method))) %>% 
    mutate(rob_assessment_method = if_else(rob_assessment_method %in% c("Not reported"), "No risk of bias reported", rob_assessment_method)) %>% 
  group_by(rob_assessment_method) %>%
  summarise(n = sum(n))

# Grouping visualization methods with count <= 3 under "Other Viz" and summing the counts
visualization_count <- visualization_count %>% 
   mutate(visualization_method = if_else(n<= 3, "Other Viz", as.character(visualization_method))) %>% 
  mutate(visualization_method = if_else(visualization_method %in% c("Not reported"), "No vizualization reported", visualization_method)) %>% 
  group_by(visualization_method) %>%
  summarise(n = sum(n))

# Grouping reporting standards types with count <= 2 under "Other guideline" and summing the counts
reporting_guide_count <- reporting_guide_count %>% 
  mutate(reporting_standards_type = ifelse(n<= 2, "Other guideline", as.character(reporting_standards_type))) %>% 
  mutate(reporting_standards_type = ifelse(reporting_standards_type %in% c("Not reported"), "No reporting guideline reported",reporting_standards_type)) %>% 
  group_by(reporting_standards_type) %>%
  summarise(n = sum(n))

# Grouping bias assessment visualization types and summing the counts
bias_visualization_count <- bias_visualization_count %>% 
  mutate(bias_assessment_visualization = if_else(bias_assessment_visualization %in% c("Doi plot", "Trim and fill", "Funnel plot", "Galbraith plot"), "Bias visualization", bias_assessment_visualization)) %>% 
  mutate(bias_assessment_visualization = if_else(bias_assessment_visualization %in% c("Not reported"), "No bias visualization reported", bias_assessment_visualization)) %>% 
  group_by(bias_assessment_visualization) %>%
  summarise(n = sum(n))

# Combine the data frames and unite the methodology types
df <- bind_rows(
  database_count %>% mutate(methodology_type = 'Database Search'),
  effectsize_count %>% mutate(methodology_type = 'Effect Size'),
  software_count %>%  mutate(methodology_type = "Software"),
  heterogeneity_count %>% mutate(methodology_type = "Heterogeneity"),
  sensitivity_count %>% mutate(methodology_type = "Sensitivity_Analysis"),
  bias_method_count %>%  mutate(methodology_type = "Bias Assessment"),
  rob_method_count %>% mutate(methodology_type = "Risk  of Bias"), 
 # visualization_count %>% mutate(methodology_type = "Visualization"),
  reporting_guide_count %>%  mutate(methodology_type = "Reporting Guide"),
  bias_visualization_count %>%  mutate(methodology_type = "Bias Assessment")
) %>% 
 unite(methodology_type_specific, 
       database_search, 
       effect_size,  
       software_analysis,
       heterogeneity_assessment_method, 
       sensitivity_analysis_method, 
       bias_assessment_method,
       rob_assessment_method, 
      # visualization_method, 
       reporting_standards_type, 
       bias_assessment_visualization,
       remove = TRUE, na.rm = TRUE)

# Preparing the edges dataframe for creating the graph
edges <- df %>%
  rename(from = methodology_type, to = methodology_type_specific, size = n) %>%
  select(c(from,to,size)) %>%
  as.data.frame()

# Preparing the vertices dataframe for creating the graph
vertices <- df  %>%
  rename(name = methodology_type_specific, size = n) %>%
  select(c(name,size)) %>%
  as.data.frame()

# Appending unique 'from' values to vertices and their corresponding summed sizes
vertices[(nrow(edges)+1):(nrow(edges)+length(unique(edges$from))),1] <- unique(edges$from)
N <- aggregate(edges$size, list(edges$from), FUN=sum)
vertices[(nrow(edges)+1):(nrow(edges)+length(unique(edges$from))),2] <- N$x

# Creating a graph object from the edges and vertices dataframes
mygraph <- graph_from_data_frame(edges, vertices = vertices)

# Plotting the graph using a 'circlepack' layout, adding labels and adjusting aesthetics

figs24 <- ggraph(mygraph, layout = 'circlepack', weight = size) + 
    geom_node_circle(aes(fill = as.factor(depth)), color = NA) +
    scale_fill_manual(values = c("#1b9e77", "#88D1AD")) + 
    geom_node_text(aes(label = name, filter = leaf, size = 10), vjust = -0.3, fontface = "bold") +
    geom_node_text(aes(label = paste0("(", size, ")"), filter = leaf, size = 10), vjust = 1, fontface = "bold") +
    theme_void() +
    theme(legend.position = "none") 

figs24

ggsave(here("figures", "figs24.pdf"), width = 18, height = 15, units = "cm", scale = 2, dpi = 800)
ggsave(here("figures", "figs24.jpg"), width = 18, height = 15, units = "cm", scale = 2, dpi = 800)
```


# Section 2 
To explore the various characteristics of the organochlorine pesticides literature such as the pesticides used, the impacts elicited in response and the subjects that were investigated.

## Figure s25
A bar plot showing the percentage and total count of total of pesticides investigate in meta-analysis investigating the impacts of organochlorine pesticides. Note: some meta-analysis may contribute to multiple sections if the study involves multiple organochlorine pesticides. Filtered for pesticide counts greater than 6. 
```{r,fig.width=16, fig.height=10}
# Function to replace long OCP names with abbreviations
replace_ocp <- function(df) {
  df %>% 
    mutate(ocp = case_when(
      # HCH related replacements
      grepl("Hexachlorocyclohexane \\(HCH\\)", ocp, ignore.case = TRUE) ~ "HCH",
      grepl("alpha-Hexachlorocyclohexane \\(alpha-HCH\\)", ocp, ignore.case = TRUE) ~ "α - HCH",
      grepl("beta-Hexachlorocyclohexane \\(beta-HCH\\)", ocp, ignore.case = TRUE) ~ "β - HCH",
      grepl("gamma-Hexachlorocyclohexane \\(gamma-HCH\\)", ocp, ignore.case = TRUE) ~ "Lindane",
      # DDT related replacements
      grepl("Dichlorodiphenyltrichloroethane \\(DDT\\)", ocp, ignore.case = TRUE) ~ "DDT",
      grepl("p,p-Dichlorodiphenyltrichloroethane \\(p,p-DDT\\)", ocp, ignore.case = TRUE) ~ "p,p-DDT",
      grepl("o,p-Dichlorodiphenyltrichloroethane \\(o,p-DDT\\)", ocp, ignore.case = TRUE) ~ "o,p-DDT",
      # DDD related replacements
      grepl("Dichlorodiphenyldichloroethane \\(DDD\\)", ocp, ignore.case = TRUE) ~ "DDD",
      grepl("p,p-Dichlorodiphenyldichloroethane \\(p,p-DDD\\)", ocp, ignore.case = TRUE) ~ "p,p-DDD",
      grepl("o,p-Dichlorodiphenyldichloroethane \\(o,p-DDD\\)", ocp, ignore.case = TRUE) ~ "o,p-DDD",
      # DDE related replacements
      grepl("Dichlorodiphenyldichloroethylene \\(DDE\\)", ocp, ignore.case = TRUE) ~ "DDE",
      grepl("p,p-Dichlorodiphenyldichloroethylene \\(p,p-DDE\\)", ocp, ignore.case = TRUE) ~ "p,p-DDE",
      grepl("o,p-Dichlorodiphenyldichloroethylene \\(o,p-DDE\\)", ocp, ignore.case = TRUE) ~ "o,p-DDE",
      TRUE ~ ocp  # no change for any others
    ))
}
# Transform the data 
ocp_count <-
  ocp %>% 
  separate_rows(ocp, sep = ",\\s+") %>% 
  replace_ocp %>% 
  count(ocp) %>% 
  filter(!is.na(ocp)) %>% # filter out NA 
  arrange(desc(n)) %>% 
  mutate(ocp = ifelse(n <= 6, "other OCP", as.character(ocp))) %>% 
# Other organochlorine pesticides include: toxaphene (n=6), Methoxychlor (n=6), p,p-DDD (n=5), o,p-DDD (n=5), cis-Chlordane (n=5), trans=Chlordane (n=4), Endosulfan II (n=4), Endosulfan (n=3),o,p-DDD (n=3), cis-Nonachlor (n=3) y-Chlordane (n=2), Chlorophenol (n=2), beta-BHC (n=2), alpha-Chlordane (n=2), TCDD (n=1), Nonachlore (n=1), Endrin ketone (n=1), Endrin Aldehyde (n=1), Endosulfan sulfate (n=1), Dicofol (n=1), delta-HCH (n=1), delta-BHC (n=1), DDD (n=1), cis-Heptachlor (n=1), alpha-BHC (n=1).
  group_by(ocp) %>% 
  summarise(n =sum(n))

# Calculate the proportion and percentage of each OCP
ocp_pct <- ocp_count %>%
  mutate(proportion = n/104,
         percentage = proportion*100)


# Create the count plot for OCPs
figs25 <- ocp_count %>%
  ggplot(aes(x = n, y = reorder(ocp, n), fill = "#1b9e77")) +
  geom_bar(stat = "identity", width = 0.8, alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(ocp, n)), hjust = 0.5, size = 7, color = "black") +
  geom_text(data = ocp_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = - 0.1, size = 7, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(name = "Article Count", expand = c(0, 0), limits = c(0, max(ocp_count$n)*1.2)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME() +
  theme(axis.text.y = element_text(size = 20))  

figs25

# ggsave(here("figures", "figs25.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs25.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
```

## Figure s26
A bar plot showing the percentage and total count of total of subjects investigate in meta-analysis investigating the impacts of organochlorine pesticides. Note: some meta-analysis may contribute to multiple sections if the study involves multiple subjects
```{r,fig.width=16, fig.height=10}
# Calculate total count for each category
subject_count <- sub %>% 
  separate_rows(subject, sep = ",\\s+") %>% 
  mutate(subject = ifelse(grepl("Non-human animal", subject, ignore.case = TRUE), "Non-human\nanimal", subject)) %>% 
  count(subject)
   

# Calculate proportion and percentage for each category
subject_pct <- subject_count %>%
  mutate(proportion = n/105,
         percentage = proportion*100)

# Create the count plot for subjects
figs26 <- subject_count %>%
  ggplot(aes(x = n, y = reorder(subject, n), fill = "#1b9e77")) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(subject, n)), hjust = 0.5, size = 12, color = "black") +
  geom_text(data = subject_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 12, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(name = "Article Count", expand = c(0, 0), limits = c(0, max(subject_count$n)*1.2)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME()   


figs26

# ggsave(here("figures", "figs26.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs26.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
```

## Figure s27
A bar plot showing the percentage and total count of total of subjects investigate in meta-analysis investigating the impacts of organochlorine pesticides. Note: some meta-analysis may contribute to multiple sections if the study involves multiple subjects. Filtered for impact counts greater than 1. 
```{r,fig.width=16, fig.height=10}
# Calculate total count for each category
impact_count <- 
  im %>% 
    separate_rows(impact, sep = ",\\s+") %>% 
    count(impact) %>% 
  filter(impact != "NA") %>%
  mutate(impact = ifelse(n<= 2, "other", as.character(impact))) %>% 
    group_by(impact) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
impact_pct <- impact_count %>%
  mutate(proportion = n/sum(impact_count$n),
         percentage = proportion*100)

# Create the count plot for impacts 
figs27 <- impact_count %>%
  ggplot(aes(x = n, y = reorder(impact, n), fill = "#1b9e77")) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(impact, n)), hjust = 0.5, size = 10, color = "black") +
  geom_text(data = impact_pct, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 10, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(name = "Article Count", expand = c(0, 0), limits = c(0, max(impact_count$n)*1.3)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME() 

figs27

# ggsave(here("figures", "figs27.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs27.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

```

## Figure s28
A bar plot showing the percentage and total count of total of impact categories investigated in meta-analysis investigating the impacts of organochlorine pesticides. 
Note: some meta-analysis may contribute to multiple sections if the study involves multiple impacts
```{r,fig.width=16, fig.height=10}
# Create a collumn for broad impacts
im <- im %>%
        separate_rows(impact, sep = ",\\s+") %>% 
  mutate(impact_broad = case_when(
    impact %in% c("parkinsons disease", "alzheimers disease", "autism spectrum disorder", "brain tumour", "amyotrophic lateral sclerosis" ) ~ "Neurological",
    impact %in% c("concentration", "contamination") ~ "Concentration",
    impact %in% c("diabetes", "thyroid function", "hypertension", "endometriosis") ~ "Endocrine",
    grepl("cancer", impact, ignore.case = TRUE) | impact %in% c("leukemia", "lymphoma", "multiple myeloma", "neuroblastoma") ~ "Carcinogen",
    impact %in% c("respiratory health", "cardiovascular disease", "asthma", "prolonged bradycardia") ~ "Cardiovascular",
 #   impact %in% c("birth outcomes", "birth weight", "preterm birth") ~ "Birth",
    impact %in% c("obesity", "adiposity") ~ "Obesity",
    impact %in% c("sperm quality", "neuroblastoma", "hypospadias", "cryptochidism", "reproductive system") ~ "Reproduction",
    TRUE ~ "Other Impact"
  ))


# Calculate total count for each category
impact_count_broad <- 
  im %>% 
    separate_rows(impact_broad, sep = ",\\s+") %>% 
    count(impact_broad) %>% 
  filter(impact_broad != "NA") %>%
    group_by(impact_broad) %>%
  summarise(n = sum(n))

# Calculate proportion and percentage for each category
impact_pct_broad <- impact_count_broad %>%
  mutate(proportion = n/105,
         percentage = proportion*100)


# Create the count plot for impacts 
figs28 <- impact_count_broad %>%
  ggplot(aes(x = n, y = reorder(impact_broad, n), fill = "#1b9e77")) +
  geom_bar(stat = "identity", width = 0.8 , alpha = 0.7) +
  geom_text(aes(label = n, x = n / 2, y = reorder(impact_broad, n)), hjust = 0.5, size = 10, color = "black") +
  geom_text(data = impact_pct_broad, aes(label = paste0("(", round(percentage, 1), "%)"), x = n), 
            hjust = -0.1, size = 10, color = "black", fontface = "bold") +
  scale_fill_identity(guide = "none") +
  scale_x_continuous(name = "Article Count", expand = c(0, 0), limits = c(0, max(impact_count_broad$n)*1.3)) +
  labs(title = NULL, x = NULL, y = NULL) +
  organochlorTHEME()

figs28

# ggsave(here("figures", "figs28.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs28.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)

```

## Figure s29
An alluvial plot showing the relationships between the pesticide of exposure, the subject being exposed and the impact of exposure 
```{r,fig.width=18, fig.height=12}

# Function to change OCP to be more general
replace_ocp2 <- function(df) {
  df %>% 
  mutate(ocp = case_when(
    grepl("Chlordane", ocp, ignore.case = TRUE) ~ "Chlordane",  
    grepl("Endosulfan", ocp, ignore.case = TRUE) ~ "Endosulfan", 
    grepl("Nonachlor", ocp, ignore.case = TRUE) ~ "Nonachlore", 
    grepl("Heptachlor", ocp, ignore.case = TRUE) ~ "Heptachlor",
    grepl("TCDD", ocp, ignore.case = TRUE) ~ "TCDD", 
    grepl("Endrin", ocp, ignore.case = TRUE) ~ "Endrin",
    grepl("Hexachlorobenzene", ocp, ignore.case = TRUE) ~ "HCH",
    grepl("Lindane", ocp, ignore.case = TRUE) ~ "HCH", 
    grepl("Hexachlorocyclohexane \\(HCH\\)", ocp, ignore.case = TRUE) ~ "HCH",
    grepl("alpha-Hexachlorocyclohexane \\(alpha-HCH\\)", ocp, ignore.case = TRUE) ~ "HCH",
    grepl("beta-Hexachlorocyclohexane \\(beta-HCH\\)", ocp, ignore.case = TRUE) ~ "HCH",
    grepl("gamma-Hexachlorocyclohexane \\(gamma-HCH\\)", ocp, ignore.case = TRUE) ~ "HCH",
    grepl("Dichlorodiphenyltrichloroethane \\(DDT\\)", ocp, ignore.case = TRUE) ~ "DDT",
    grepl("p,p-Dichlorodiphenyltrichloroethane \\(p,p-DDT\\)", ocp, ignore.case = TRUE) ~ "DDT",
    grepl("o,p-Dichlorodiphenyltrichloroethane \\(o,p-DDT\\)", ocp, ignore.case = TRUE) ~ "DDT",
    grepl("Dichlorodiphenyldichloroethane \\(DDD\\)", ocp, ignore.case = TRUE) ~ "DDD",
    grepl("p,p-Dichlorodiphenyldichloroethane \\(p,p-DDD\\)", ocp, ignore.case = TRUE) ~ "DDD",
    grepl("o,p-Dichlorodiphenyldichloroethane \\(o,p-DDD\\)", ocp, ignore.case = TRUE) ~ "DDD",
    grepl("Dichlorodiphenyldichloroethylene \\(DDE\\)", ocp, ignore.case = TRUE) ~ "DDE",
    grepl("p,p-Dichlorodiphenyldichloroethylene \\(p,p-DDE\\)", ocp, ignore.case = TRUE) ~ "DDE",
    grepl("o,p-Dichlorodiphenyldichloroethylene \\(o,p-DDE\\)", ocp, ignore.case = TRUE) ~ "DDE",
    TRUE ~ ocp
  ))
}
 
# Transform the data 
alluvial <- im %>%
  left_join(ocp, by = "study_id") %>%
  left_join(sub, by = "study_id") %>%
  separate_rows(subject, sep = ",\\s+") %>% 
  separate_rows(ocp, sep = ",\\s+") %>% 
  separate_rows(impact_broad, sep = ",\\s+") %>%
  replace_ocp2() %>%
  filter(!grepl("not reported", ocp, ignore.case = TRUE)) %>%
  group_by(ocp, subject, impact_broad) %>%
  summarise(freq = n(), .groups = 'drop') %>%
  group_by(ocp) %>%
  filter(sum(freq) > 10) %>%  
  group_by(impact_broad) %>% 
  filter(sum(freq) > 5) %>% 
  mutate(subject = factor(subject, levels = c("Environment", "Non-human animal", "Human"), ordered = TRUE))

# Make alluvial plot
figs29 <- alluvial %>% 
  ggplot(
       aes(axis1 = ocp, axis2 = subject, axis3 = impact_broad, y = freq)) +
  scale_x_discrete(limits = c("Organochlorine Pesticide", "Subject", "Impact"), expand = c(.05, .10)) +
  xlab("Variables") +
  ylab("Frequency") +  
  geom_alluvium(aes(fill = subject)) +
  geom_stratum(width = 1/2, fill = "white", color = "black") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 5, fontface = "bold") +  
  theme_minimal() +
  theme(axis.text=element_text(size=15),  
        axis.title=element_text(size=20),
        legend.position="none",
        panel.grid.major = element_blank(),  
        panel.grid.minor = element_blank()) + 
  scale_fill_brewer(palette = "Dark2", name = "Subject Category")

figs29

# ggsave(here("figures", "figs29.pdf"), width =18, height = 12, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs29.jpg"), width = 18, height = 12, units = "cm", scale = 2, dpi = 800)
```


## Figure 4
A bubble plot showing the counts of each pesticide per impact included in current meta-analysis on the impacts of organochlorine pesticides 
```{r,fig.width=25, fig.height=15}

# Join the organochlorine pesticide details with the impact details
ocp_im <- left_join(ocp, im ,sub, by = "study_id") 

# Separate rows in "ocp_im"
ocp_im1 <- separate_rows(ocp_im, ocp , sep = ", ", convert = TRUE)


# Group by "ocp" and "impact" and summarize count
ocp_im_summary <- ocp_im1 %>%
  mutate(ocp = str_trim(ocp),
         impact_broad = str_trim(impact_broad)) %>%
    replace_ocp2() %>% 
  group_by(ocp, impact_broad) %>%
  summarise(count = n(), .groups = "drop") %>% 
  mutate(ocp = ifelse(grepl("no chemical classification", ocp, ignore.case = TRUE), "no chemical\n classification", ocp))

# Filter for top 8 pesticides 
top_pesticides <- ocp_im_summary %>%
  filter(ocp != "not reported") %>%
  group_by(ocp) %>%
  summarise(total_count = sum(count)) %>%
  top_n(8, total_count) %>%
  pull(ocp) 


ocp_im_summary_filtered <- ocp_im_summary %>%
  filter(ocp %in% top_pesticides)

fig4 <- ocp_im_summary_filtered %>% 
  ggplot(aes(x = fct_rev(fct_reorder(impact_broad, count, .fun = 'sum')),
             y = fct_reorder(ocp, count, .fun = 'sum'),
             size = count,
             fill = impact_broad)) +
  geom_point(shape = 21, color = "white") +
  geom_text(aes(label = count), size = 20, fontface = "bold") +
  labs(title = NULL, x = NULL, y =NULL) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(size = 35),  
    axis.text.y = element_text(size = 40, hjust = 1),  
    legend.position = "none") +
  scale_size_continuous(range = c(30, 80))

fig4

 ggsave(here("figures", "fig4.pdf"), width = 40, height = 25, units = "cm", scale = 2, dpi = 800)
 ggsave(here("figures", "fig4.jpg"), width = 40, height = 25, units = "cm", scale = 2, dpi = 800)
  
```

# Section 3
To investigate global research output and collaboration networks

## Figure s30, s31, s32, s33 & s34
```{r,fig.width=16, fig.height=10}

bibliometric_analysis <- biblioAnalysis(bib_sco)
par(cex=1.5)
plot(bibliometric_analysisbibliometric_analysis)
summary(bibliometric_analysis)
str(bibliometric_analysis)

```

## Figure s33
Most productive countries for meta-analyses included in the systematic review map Blue is for single country publications (i.e., countries with authors from a single country) and red is for multiple country publications (i.e., countries with authors from multiple countries).
```{r,fig.width=16, fig.height=10}
figs30_data <- bibliometric_analysis$CountryCollaboration
figs30_tibble <- as_tibble(figs33_data)

figs30_tibble_long <- figs30_tibble %>% 
                      pivot_longer(cols = c(SCP, MCP), names_to = "Country_Collaboration", values_to = "value")


# Summarizing and filtering for top ten countries
top_countries <- figs30_tibble_long %>%
  group_by(Country) %>%
  summarize(Total = sum(value)) %>%
  arrange(desc(Total)) %>%
  top_n(10, Total)

# Filtering the original dataset to keep only the top ten countries
figs30_tibble_long_filtered <- figs30_tibble_long %>%
  filter(Country %in% top_countries$Country)


figs30 <- ggplot(figs30_tibble_long_filtered, aes(fill = Country_Collaboration, y = value, x = Country)) +
  geom_bar(position = "stack", stat = "identity") +
  coord_flip() +
  organochlorTHEME()

figs30

# ggsave(here("figures", "figs30.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs30.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800) 

```

##  Figure s31
```{r,fig.width=16, fig.height=10}

figs31_data <- bibliometric_analysis$Authors
figs31_tibble <- as_tibble(figs34_data)

figs31_tibble_filtered <- figs34_tibble %>% 
                          slice_head(n=10) 
  

figs31 <- ggplot(figs31_tibble_filtered, aes(y = n, x = reorder(AU,n))) +
          geom_bar(stat = "identity", width = 0.8 , alpha = 0.7, fill = "#1b9e77") +
          geom_text(aes(label = n, y = n/2), hjust = 0.5, color = "black", size = 10) +
          coord_flip() +
          organochlorTHEME()

# ggsave(here("figures", "figs31.pdf"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs31.jpg"), width = 16, height = 10, units = "cm", scale = 2, dpi = 800) 

```


## Figure 5 
Heat map of world showing the country-level counts for first authors’ country of affiliation of meta-analysis investigating the impacts of organochlorine pesticides. Grey indicates no publications affiliated with a given country in our data set. 
```{r,fig.width= 21, fig.height=12}
# Extract country information from the "AU1_CO" and "AU_CO" fields of the "bib_sco" dataset
bibmap <- metaTagExtraction(bib_sco, Field = "AU1_CO", sep = ";") 
bibmap <- metaTagExtraction(bibmap, Field = "AU_CO", sep = ";") 

# Create a data frame with counts of articles from each country
firstcountrycounts <- bibmap %>% 
  group_by(AU1_CO) %>% 
  count() %>% 
  filter(!is.na(AU1_CO))  

# Load world map data and remove countries with longitude >180 to make an equal projection-like map
world_map <- map_data("world") %>% 
  filter(! long > 180)

# Format country names to match regions on the world map
firstcountrycounts$region <- str_to_title(firstcountrycounts$AU1_CO)
firstcountrycounts$region[firstcountrycounts$region == "Usa"] <- "USA" 
firstcountrycounts$region[firstcountrycounts$region == "Korea"] <- "South Korea"
firstcountrycounts$region[firstcountrycounts$region == "United Kingdom"] <- "UK"

# Join count data with map data and set missing counts to zero
emptymap <- tibble(region = unique(world_map$region), n = rep(0,length(unique(world_map$region))))
fullmap <- left_join(emptymap, firstcountrycounts, by = "region")
fullmap$n <- fullmap$n.x + fullmap$n.y
fullmap$n[is.na(fullmap$n)] <- 0

fig5 <- fullmap %>%
  ggplot(aes(fill = n, map_id = region)) +
  geom_map(map = world_map, color = "gray50") +
  expand_limits(x = world_map$long, y = world_map$lat) +
  coord_map("mercator", xlim = c(-180, 180), ylim = c(-50, 90)) +  
  theme(
    axis.text = element_blank(),  
    axis.title = element_blank(),  
    legend.position = "bottom",
    legend.box = "horizontal",  
    legend.box.just = "center",  
    legend.margin = margin(t = 10, unit = "pt"),  
    legend.text = element_text(size = 20),  
    legend.title = element_text(size = 20, face = "bold"),  
    legend.key.width = unit(30, "mm"), 
    panel.background = element_rect(fill = "white")
  ) +
  scale_fill_gradient(
    low = "#98FB98", high = "#006400",
    name = "First Author Count",  
    na.value = "gray70",
    limits = c(1, 20)
  ) +
  guides(
    fill = guide_colourbar(
      barwidth = unit(210, units = "mm"),
      barheight = unit(5, units = "mm")
    )
  )

fig5

 # ggsave(here("figures", "fig5.pdf"), width = 21, height = 12, units = "cm", scale = 2, dpi = 800)
 # ggsave(here("figures", "fig5.jpg"), width = 21, height = 12, units = "cm", scale = 2, dpi = 800)

```

## Figure s38
Heat map of Europe showing the country-level counts for first authors’ country of affiliation of meta-analysis investigating the impacts of organochlorine pesticides. Grey indicates no publications affiliated with a given country in our data set. 
```{r,fig.width= 21, fig.height=12}
figs38 <- fullmap %>%
ggplot(aes(fill = n, map_id = region)) +
geom_map(map = world_map, color = "gray50") +
coord_map("mercator", ylim = c(35, 65), xlim = c(-35, 45)) + 
theme(
    axis.text = element_blank(),  
    axis.title = element_blank(),  
    legend.box = "horizontal",  
    legend.box.just = "center",  
    legend.margin = margin(t = 10, unit = "pt"),  
    legend.text = element_text(size = 16),  
    legend.title = element_text(size = 18, face = "bold"),  
    legend.key.width = unit(30, "mm"),
    legend.position = "bottom",
    panel.background = element_rect(fill = "white")
) +
scale_fill_gradient(low = "#98FB98", high = "#006400",
    name = "First Author Count", na.value = "gray70",
    limits = c(1, 10), breaks = seq(0, 10, length.out = 6)) 

figs38

# ggsave(here("figures", "figs38.pdf"), width = 16, height = 12, units = "cm", scale = 2, dpi = 800)
# ggsave(here("figures", "figs38.jpg"), width = 16, height = 12, units = "cm", scale = 2, dpi = 800)
```


## Figure s39
Chord diagram of collaborations across countries. Countries represent the location of the primary authors’ affiliated institution. 
```{r,fig.width= 12, fig.height=12}

jpeg(filename = here("figures", "figs39.jpg"), width = 800, height = 800)

# Extract countries from the affiliations
bib_sco2 <- metaTagExtraction(bib_sco, Field = "AU_CO", sep = ";")

# Create a network matrix of collaborations between countries
NetMatrix_country <- biblioNetwork(bib_sco2, analysis = "collaboration", network = "countries", sep = ";")

# Convert the network matrix to a standard matrix
NetMatrix_country <- as.matrix(NetMatrix_country)

# Remove the lower triangle (as this is duplication of info)
NetMatrix_country[lower.tri(NetMatrix_country)] <- 0 

# Change column and row names to title case
colnames(NetMatrix_country) <- str_to_title(colnames(NetMatrix_country))
rownames(NetMatrix_country) <- str_to_title(rownames(NetMatrix_country))

# Change "Usa" to "USA"
colnames(NetMatrix_country)[colnames(NetMatrix_country) == "Usa"] <- "USA"
rownames(NetMatrix_country)[rownames(NetMatrix_country) == "Usa"] <- "USA"

# Change "United Kingdom" to "UK" for easier plotting
colnames(NetMatrix_country)[colnames(NetMatrix_country) == "United Kingdom"] <- "UK"
rownames(NetMatrix_country)[rownames(NetMatrix_country) == "United Kingdom"] <- "UK"

# Setting up custom parameters
circos.par(cell.padding = c(0, 0, 0, 0), track.margin = c(0, 0))

# Create a chord diagram of the network matrix
 chordDiagram(NetMatrix_country, annotationTrack = "grid", preAllocateTracks = 1)

# Add a track to label each sector with its name
circos.trackPlotRegion(track.index = 1, bg.border = NA, panel.fun = function(x, y) {
  xlim = get.cell.meta.data("xlim")
  ylim = get.cell.meta.data("ylim")
  sector.name = get.cell.meta.data("sector.index")
  circos.text(mean(xlim), ylim[1] + 0.2 , sector.name, facing = "clockwise", niceFacing = TRUE, adj = c(0, 0))
  circos.axis(h = "top", labels.cex = 0.5, major.tick.length = 0.2, sector.index = sector.name, track.index = 2)
})

dev.off()
```

## Figure s40
Chord diagram illustration of collaborations across countries. Countries represent the location of the primary authors’ affiliated institution. Collaborations within countries are not shown.
```{r,fig.width= 12, fig.height=12}
jpeg(filename = here("figures", "figs40.jpg"), width = 800, height = 800)

diag(NetMatrix_country) <- 0

# Create a chord diagram of the network matrix
figs43 <- chordDiagram(NetMatrix_country, annotationTrack = "grid", preAllocateTracks = 1)

# Add a track to label each sector with its name
circos.trackPlotRegion(track.index = 1, panel.fun = function(x, y) {
  xlim = get.cell.meta.data("xlim")
  ylim = get.cell.meta.data("ylim")
  sector.name = get.cell.meta.data("sector.index")
  circos.text(mean(xlim), ylim[1] + 0.2, sector.name, facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.5))
  circos.axis(h = "top", labels.cex = 0.5, major.tick.length = 0.2, sector.index = sector.name, track.index = 2)
}, bg.border = NA)

dev.off()

```

## Figure 6
Chord diagram illustration of collaborations across continents. Continents represent the location of the primary authors’ affiliated institution. Collaborations within countries are not shown.
```{r,fig.width= 12, fig.height=12}
# Country rename to continents
NetMatrix_continent <- NetMatrix_country
colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "USA"] <- "North America"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "USA"] <- "North America"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Spain"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Spain"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "China"] <- "Asia"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "China"] <- "Asia"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "France"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "France"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Canada"] <- "North America"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Canada"] <- "North America"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Denmark"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Denmark"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Netherlands"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Netherlands"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Belgium"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Belgium"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "UK"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "UK"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Australia"] <- "Other"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Australia"] <- "Other"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Brazil"] <- "Other"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Brazil"] <- "Other"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Germany"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Germany"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Finland"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Finland"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Greece"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Greece"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Korea"] <- "Asia"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Korea"] <- "Asia"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Norway"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Norway"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Sweden"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Sweden"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Egypt"] <- "Other"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Egypt"] <- "Other"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Italy"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Italy"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Japan"] <- "Asia"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Japan"] <- "Asia"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Portugal"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Portugal"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Switzerland"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Switzerland"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Turkey"] <- "Asia"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Turkey"] <- "Asia"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "India"] <- "Asia"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "India"] <- "Asia"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Iran"] <- "Asia"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Iran"] <- "Asia"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Costa Rica"] <- "North America"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Costa Rica"] <- "North America"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Czech Republic"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Czech Republic"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Hong Kong"] <- "Asia"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Hong Kong"] <- "Asia"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Iceland"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Iceland"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Ireland"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Ireland"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Mexico"] <- "North America"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Mexico"] <- "North America"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Romania"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Romania"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Slovakia"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Slovakia"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Ukraine"] <- "Europe"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Ukraine"] <- "Europe"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Australia"] <- "Other"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Australia"] <- "Other"

colnames(NetMatrix_continent)[colnames(NetMatrix_continent) == "Kazakhstan"] <- "Asia"
rownames(NetMatrix_continent)[rownames(NetMatrix_continent) == "Kazakhstan"] <- "Asia"


# collapsing
merge_matrix <- t(rowsum(t(NetMatrix_continent), group = colnames(NetMatrix_continent), na.rm = T))
merge_matrix2 <- rowsum(merge_matrix, group = rownames(merge_matrix))


# remove diagonal elements
diag(merge_matrix2) <- 0

# chord plot
chordDiagramFromMatrix(merge_matrix2)


# Define colors
my_colors <- c("#1B9E77", "#D95F02", "#7570B3", "#E6AB02")

my_colors <- rep(my_colors, length.out = ncol(merge_matrix2))


jpeg("figures//figs42.jpeg", width = 16, height = 16, units = "cm", res = 300)

# Create a chord diagram of the network matrix
fig6 <- chordDiagram(small_matrix, 
                       annotationTrack = "grid", 
                       annotationTrackHeight = mm_h(7), 
                       preAllocateTracks = 1,
                       grid.col = my_colors2)

# Add a track to label each sector with its name
circos.trackPlotRegion(track.index = 1, panel.fun = function(x, y) {
  xlim = get.cell.meta.data("xlim")
  ylim = get.cell.meta.data("ylim")
  sector.name = get.cell.meta.data("sector.index")
  circos.text(mean(xlim), ylim[1], sector.name, facing = "bending.inside", niceFacing = TRUE, adj = c(0.5, 1.75), col = "white", cex = 1.2)
}, bg.border = NA)


dev.off()

```


## Figure s45
Chord diagram illustration of collaborations across disciplines. Disciplines have been allocated based on the Journal Citation Categories on Web of Science. Collaborations within disciplines are not shown.
```{r,fig.width= 12, fig.height=12}
fields <- sd %>%
  mutate(
    title = str_to_lower(paper_title),
    ntitle = paste(str_to_lower(str_split_fixed(study_id, "\\_", n = 2)[,1]), paper_title, sep = " "),
    ntitle = str_squish(ntitle),
    Journal_Category_Allocated_Broad = str_replace_all(Journal_Category_Allocated_Broad, "Toxicology", "Other"),
    Journal_Category_Allocated_Broad = str_replace_all(Journal_Category_Allocated_Broad, "Multidisciplinary", "Multi\ndisciplinary"),
    Journal_Category_Allocated_Broad = str_replace_all(Journal_Category_Allocated_Broad, "Marine Biology", "Other"),
    Journal_Category_Allocated_Broad = str_replace_all(Journal_Category_Allocated_Broad, "Environmental Science", "Environemental\nScience"),    Journal_Category_Allocated_Broad = str_replace_all(Journal_Category_Allocated_Broad, "Psychology", "Other")
  ) %>% 
  select(ntitle, Journal_Category_Allocated_Broad)

bib_names <- bib_sco %>% rownames_to_column(., var = "mat_names") %>% 
  mutate(TI2 = tolower(unlist(lapply(data.frame(t(str_split_fixed(TI, " ", n = 15)[,1:14])), 
                                     function(x) str_c(x, collapse  = " ")))),
         name2 = stri_trans_general(tolower(str_split_fixed(SR, " ", n = 2)[,1]), "latin-ascii"),
         TI2 = paste(name2, TI2, sep= " "),
         TI2 = trimws(TI2)) %>% 
  select(TI2, mat_names)

pos <- lapply(bib_names$TI2, function(x) stringdist(fields$ntitle, x))
pos2<- map_dbl(pos, which.min)

# now we can merge two datasets 
bib_names$Journal_Category_Allocated_Broad <- fields$Journal_Category_Allocated_Broad[pos2]

# Creating matrix for bibliometric coupling
NetMatrix <- biblioNetwork(bib_sco, analysis = "coupling", network = "references", sep = ";")

# forcing into a nromal matrix
net_matrix <- as.matrix(NetMatrix)

diag(net_matrix) <- 0 #get rid of counts for the same papers

# replacing names with Journal_Category_Allocated_Broad
rownames(net_matrix) <- bib_names$Journal_Category_Allocated_Broad
colnames(net_matrix) <- bib_names$Journal_Category_Allocated_Broad

# reducing matrix according to discipline_code
rect_matrix<- t(rowsum(t(net_matrix), group = colnames(net_matrix), na.rm = T))
small_matrix <- rowsum(rect_matrix, group = rownames(rect_matrix))

# getting rid of lower triangle (as this is duplication of info)
small_matrix[lower.tri(small_matrix)] <- 0 
par(mar = c(0, 0, 0, 0), mfrow = c(1, 1))

# diag(small_matrix) <- 0

# Define colors
my_colors2 <- c("#1B9E77", "#D95F02", "#7570B3",  "#E6AB02")

my_colors2 <- rep(my_colors2, length.out = ncol(small_matrix))

jpeg("figures//figs42.jpeg", width = 20, height = 20, units = "cm", res = 300)

# Create a chord diagram with specified colors
figs47 <- chordDiagram(small_matrix, 
                       annotationTrack = "grid", 
                       annotationTrackHeight = mm_h(7), 
                       preAllocateTracks = 1,
                       grid.col = my_colors2)

# Add a track to label each sector with its name
circos.trackPlotRegion(track.index = 1, panel.fun = function(x, y) {
  xlim = get.cell.meta.data("xlim")
  ylim = get.cell.meta.data("ylim")
  sector.name = get.cell.meta.data("sector.index")
  circos.text(mean(xlim), ylim[1] , sector.name, facing = "clockwise", niceFacing = TRUE, adj = c(-0.1, 0.5), col = "black", cex = 1)
}, bg.border = NA)

dev.off()
```

